Setup¶

Retrieve "GL-DPPD-7111_Mmus_Brain_CellType_GeneMarkers.csv" from this GitHub link and save it in the same directory as this notebook. The GitHub path is nasa/GeneLab_Data_Processing/scRNAseq/10X_Chromium_3prime_Data/GeneLab_CellType_GeneMarkers/GL-DPPD-7111_GeneMarker_Files.

Possible Map My Cells region keys = ["RHP", "RSP", "ACA", "PL-ILA-ORB", "AUD-TEa-PERI-ECT", "SS-GU-VISC", "MO-FRP", "PAL", "sAMY", "CTXsp", "HY", "STRv", "OLF", "LSX", "AI", "STRd", "VIS-PTLp", "VIS", "TH", "MOp", "ENT", "HIP", "P", "MB", "MY", "CB", "AUD", "SSp", "TEa-PERI-ECT"]

Use Conda

While in the folder containing this notebook: conda env create -f rapidsc.yml

Navigate back to where you want to clone the scflow repository (I recommend home) cd

Clone scflow from GitHub. git clone git@github.com:easlinger/scflow.git

Navigate to the folder where scflow is: pip install .

pip install senepy

For NVIDIA Drivers (Linux)

sudo apt update
sudo apt install -y build-essential dkms

sudo apt install -y wget
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"

sudo apt update
sudo apt install -y cuda

Imports & Display¶

In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline

import os
import re
import logging
import json
try:
    import torch
    torch.set_float32_matmul_precision("medium")
except Exception:
    pass
import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import pandas as pd
import numpy as np
import scflow

pd.set_option("display.max_rows", 500)  # or None for unlimited rows
pd.set_option("display.max_columns", 100)
pd.set_option("display.width", 200)


class CategoricalFilter(logging.Filter):
    def filter(self, record):
        return "storing" not in record.getMessage() or \
               "as categorical" not in record.getMessage()


logger = logging.getLogger("anndata")
logger.addFilter(CategoricalFilter())

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/jaxopt/__init__.py:59: DeprecationWarning: JAXopt is no longer maintained. See https://docs.jax.dev/en/latest/ for alternatives.
  warnings.warn(
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/__init__.py:17: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
  __version_info__ = tuple(LooseVersion(__version__).version)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/fields.py:198: RemovedInMarshmallow4Warning: Passing field metadata as a keyword arg is deprecated. Use the explicit `metadata=...` argument instead.
  warnings.warn(
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pkg_resources/__init__.py:3146: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('sphinxcontrib')`.
Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
  declare_namespace(pkg)

Set Options (ACTIVELY SET THESE!)¶

In [2]:
# Processors to Use
n_processors = os.cpu_count() - 1  # how many processors to use

# If You Want Results Emailed
cur_file = os.path.join(os.path.abspath(""), "create_object_613.ipynb")
html_out = os.path.splitext(cur_file)[0] + ".html"
email = "elizabeth.aslinger@aya.yale.edu"
# set email to None to skip

# Set Data Sources & Species
species = "Mouse"
batches = ["OSD-613"]  # in list even if just 1, or list multiple to integrate

# Set Source Data Directory & Output Options
superdirec = "/home/easlinger/data"  # directory with original data
direcs = [os.path.join(superdirec, i) for i in batches]
# new h5ad will write to "data" sub-directory of where this notebook is
overwrite = True  # allow overwrite of files?
file_concat = os.path.join("data", f"{'_'.join(batches)}_concatenated.h5ad")
file_new = os.path.join("data", f"{'_'.join(batches)}_integrated.h5ad")

# Set Sample & Batch IDs, Plus Other Potential Sources of Batch Effects
col_group = "Group"  # age &/or space flight
col_age = "Characteristics[Age at Euthanasia]"
# col_age = "Factor Value[Age]"
col_condition = "Factor Value[Spaceflight]"
col_sample = "sample"
col_batch = "batch" if len(
    batches) > 1 else col_group  # group=batch if 1 dataset
covariates_categorical = ["Comment[Euthanasia Date]"]
# covariates_categorical = None

# Do Sub-Clustering?
# subcluster_biggest = 1  # sub-cluster biggest cluster
# subcluster_biggest = 3  # sub-cluster biggest 3 clusters
subcluster_biggest = False  # no sub-clustering
kws_cluster = dict()  # cluster individual samples
# kws_cluster = None  # do not cluster individual samples

# Set Annotation Sources
map_my_cells_source = "WMB-10X" if species == "Mouse" else "WHB-10X" if (
    species == "Human") else None  # Map My Cells atlas source
# map_my_cells_region_keys = None
map_my_cells_region_keys = [
    "RSP", "ACA", "PL-ILA-ORB", "AUD-TEa-PERI-ECT", "SS-GU-VISC", "MO-FRP",
    "AI", "VIS-PTLp", "VIS", "MOp", "AUD", "SSp",
    "TEa-PERI-ECT"]  # regional subset for Map My Cells
map_my_cells_cell_keys = ["Isocortex"]  # pattern match: feature name column
if species == "Mouse":
    model_celltypist = "Mouse_Whole_Brain.pkl"
else:
    raise ValueError("Manually set CellTypist model for non-mouse!")
source_patterns = ["Brain", "Cortical", "cortex"]  # for ToppGene

# Make Pre-Defined Marker Dictionary
cts_superhierarchical = {
    "Neuron": ["Excitatory", "Inhibitory", "Glutamatergic", "GABAergic",
               "Dopaminergic", "Serotonergic", "Cholinergic"]
}  # if classified as Neuron + other, just keep more specific type(s)
rename_marker_based_annotation = {
    "Excitatory | Inhibitory": "Excitatory-Inhibitory",
    "Inhibitory | Inhibitory": "Excitatory-Inhibitory"
}
mks_a_priori = pd.read_csv("GL-DPPD-7111_Mmus_Brain_CellType_GeneMarkers.csv")
mks_a_priori.loc[:, "cellName"] = mks_a_priori.cellName.replace({
    "Oligodendrocyte precursor cell": "OPC"}).apply(
        lambda x: " ".join([i.capitalize() for i in re.sub(
            " cell", "", x).split(" ")])).replace({
                "Opc": "OPC"})  # capitalize cell names
mks_a_priori = mks_a_priori.set_index("cellName")["geneSymbol"]
mks_a_priori = dict(mks_a_priori.apply(lambda x: set(x.split(","))))
mks_a_priori.update({
    "Neuroepithelial": {"Nes", "Notch1", "Sox2", "Sox10", "Hes1", "Hes3"},
    "Excitatory": {"Slc17a7", "Slc30a3", "Tcf4",
                   "Slc17a6", "Slc6a1", "Baiap3",
                   "Grin1", "Grin2b", "Gls"},
    "Inhibitory": {"Gad1", "Slc6a1", "Gabbr1", "Gabbr2",
                   "Gad2", "Slc32a1", "Oprm1", "Htr2c"},
    # "Glutamatergic": {"Slc17a7", "Slc17a6", "Grin1", "Grin2b", "Gls"},
    # "GABAergic": {"Slc6a1", "Gabbr1", "Gabbr2", "Gad2", "Gad1"},
    # "Dopaminergic": {"Th", "Dat", "Foxa2", "Girk2", "Nurr1", "Lmx1b"},
    # "Serotonergic": {"Tph", "Sert", "Pet1"},
    # "Cholinergic": {"ChAT", "VAChT", "Acetylcholinesterase"}
})
markers_predefined = {**mks_a_priori}
if cts_superhierarchical is not None:
    for i in cts_superhierarchical:
        for k in [u for u in cts_superhierarchical[
                i] if u in markers_predefined]:
            markers_predefined[k] = markers_predefined[k].union(
                markers_predefined[i])
    _ = [markers_predefined.pop(i) for i in cts_superhierarchical]
mks_collapsed = {**mks_a_priori}
mks_collapsed["Neuron"] = mks_collapsed["Neuron"].union(mks_collapsed[
    "Excitatory"]).union(mks_collapsed["Inhibitory"])
_ = mks_collapsed.pop("Excitatory")
_ = mks_collapsed.pop("Inhibitory")

Load Individual Sample Data¶

In [3]:
%%time

# Create a Subdirectory of Working Directory for Data Outputs
os.makedirs("data", exist_ok=True)

# Get Metadata
metadata = [pd.read_csv(os.path.join(
    superdirec, f"{i}_metadata_{i}-ISA/s_{i}.txt"), sep=None,
                        engine="python").set_index("Source Name").rename_axis(
                            col_sample) for i in batches]  # list of metadata
for u in np.arange(len(metadata)):  # add a combined age & condition variable
    metadata[u] = metadata[u].join(metadata[u].apply(
        lambda x: x[col_condition] + str(
            " | " + str(x[col_age]) + " Weeks" if (
                col_age in metadata[u].columns and len(
                    metadata[u][col_age].unique()) > 1) else ""),
        axis=1).to_frame(col_group))  # add space flight (x age if applicable)

# Load Data
adatas, files = {}, {}
for u, d in enumerate(direcs):  # iterate directories, then samples within
    for x in [i for i in os.listdir(d) if os.path.isdir(os.path.join(d, i))]:
        ddd = [os.path.join(d, x, i) for i in os.listdir(os.path.join(d, x))]
        if len(ddd) > 1:  # ensure 1 subdirectory (for the sample) in folder
            raise ValueError(f"More than one file: {ddd}")
        sample = os.path.basename(os.path.join(d, x, ddd[0]))  # sample ID
        files[sample] = os.path.join(os.path.join(d, x, ddd[0]),
                                     "filtered_feature_bc_matrix.h5")  # file
        adatas[sample] = sc.read_10x_h5(files[sample])  # read anndata (h5)
        if col_batch:  # if a batch column specified
            adatas[sample].obs.loc[:, col_batch] = batches[u]  # batch => .obs
        adatas[sample].obs.loc[:, col_sample] = sample  # sample ID => .obs
        samp_metadata = metadata[u].loc[sample] if (
            sample in metadata[u].index.values) else metadata[u].set_index(
                "Sample Name").loc[sample]  # extract sample-specific metadata
        for v in samp_metadata.index.values:  # loop metadata => .obs columns
            adatas[sample].obs.loc[:, v] = samp_metadata.loc[v]
        adatas[sample].obs.loc[:, f"n_cells_original_{col_sample}"] = adatas[
            sample].obs.shape[0]  # original number of cells
print(files)
metadata

{'RRRM2_BRN_GC_ISS-T_YNG_GY4': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_YNG_GY4-20250307T191036Z-001/RRRM2_BRN_GC_ISS-T_YNG_GY4/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_YNG_GY9': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_YNG_GY9-20250307T192252Z-001/RRRM2_BRN_GC_ISS-T_YNG_GY9/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_OLD_GO18': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_OLD_GO18-20250307T185055Z-001/RRRM2_BRN_GC_ISS-T_OLD_GO18/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_OLD_FO20': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_OLD_FO20-20250307T162503Z-001/RRRM2_BRN_FLT_ISS-T_OLD_FO20/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_OLD_GO19': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_OLD_GO19-20250307T185555Z-001/RRRM2_BRN_GC_ISS-T_OLD_GO19/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_OLD_GO13': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_OLD_GO13-20250307T164505Z-001/RRRM2_BRN_GC_ISS-T_OLD_GO13/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_YNG_FY8': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_YNG_FY8-20250307T163836Z-001/RRRM2_BRN_FLT_ISS-T_YNG_FY8/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_YNG_FY7': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_YNG_FY7-20250307T164138Z-001/RRRM2_BRN_FLT_ISS-T_YNG_FY7/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_OLD_FO19': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_OLD_FO19-20250307T161920Z-001/RRRM2_BRN_FLT_ISS-T_OLD_FO19/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_YNG_GY7': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_YNG_GY7-20250307T191343Z-001/RRRM2_BRN_GC_ISS-T_YNG_GY7/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_OLD_FO14': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_OLD_FO14-20250307T160756Z-001/RRRM2_BRN_FLT_ISS-T_OLD_FO14/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_YNG_GY1': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_YNG_GY1-20250307T190033Z-001/RRRM2_BRN_GC_ISS-T_YNG_GY1/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_YNG_FY2': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_YNG_FY2-20250307T162616Z-001/RRRM2_BRN_FLT_ISS-T_YNG_FY2/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_OLD_FO17': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_OLD_FO17-20250307T192814Z-001/RRRM2_BRN_FLT_ISS-T_OLD_FO17/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_OLD_GO16': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_OLD_GO16-20250307T184654Z-001/RRRM2_BRN_GC_ISS-T_OLD_GO16/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_OLD_FO16': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_OLD_FO16-20250307T161325Z-001/RRRM2_BRN_FLT_ISS-T_OLD_FO16/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_GC_ISS-T_YNG_GY2': '/home/easlinger/data/OSD-613/RRRM2_BRN_GC_ISS-T_YNG_GY2-20250307T190542Z-001/RRRM2_BRN_GC_ISS-T_YNG_GY2/filtered_feature_bc_matrix.h5', 'RRRM2_BRN_FLT_ISS-T_YNG_FY5': '/home/easlinger/data/OSD-613/RRRM2_BRN_FLT_ISS-T_YNG_FY5-20250307T163216Z-001/RRRM2_BRN_FLT_ISS-T_YNG_FY5/filtered_feature_bc_matrix.h5'}
CPU times: user 16.2 s, sys: 1.66 s, total: 17.9 s
Wall time: 17.8 s
Out[3]:
[                                    Sample Name Characteristics[Organism] Term Source REF                              Term Accession Number Characteristics[Strain] Term Source REF.1  \
 sample                                                                                                                                                                                   
 RRRM-2_FL_ISS_14   RRRM2_BRN_FLT_ISS-T_OLD_FO14              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_16   RRRM2_BRN_FLT_ISS-T_OLD_FO16              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_17   RRRM2_BRN_FLT_ISS-T_OLD_FO17              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_19   RRRM2_BRN_FLT_ISS-T_OLD_FO19              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_20   RRRM2_BRN_FLT_ISS-T_OLD_FO20              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_02    RRRM2_BRN_FLT_ISS-T_YNG_FY2              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_05    RRRM2_BRN_FLT_ISS-T_YNG_FY5              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_07    RRRM2_BRN_FLT_ISS-T_YNG_FY7              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_FL_ISS_08    RRRM2_BRN_FLT_ISS-T_YNG_FY8              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_13   RRRM2_BRN_GC_ISS-T_OLD_GO13              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_16   RRRM2_BRN_GC_ISS-T_OLD_GO16              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_18   RRRM2_BRN_GC_ISS-T_OLD_GO18              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_19   RRRM2_BRN_GC_ISS-T_OLD_GO19              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_01    RRRM2_BRN_GC_ISS-T_YNG_GY1              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_02    RRRM2_BRN_GC_ISS-T_YNG_GY2              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_04    RRRM2_BRN_GC_ISS-T_YNG_GY4              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_07    RRRM2_BRN_GC_ISS-T_YNG_GY7              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 RRRM-2_HGC_ISS_09    RRRM2_BRN_GC_ISS-T_YNG_GY9              Mus musculus       NCBITAXON  http://purl.bioontology.org/ontology/NCBITAXON...             C57BL/6NTac               EFO   
 
                                 Term Accession Number.1 Characteristics[Genotype] Term Source REF.2                     Term Accession Number.2 Characteristics[Animal Source] Characteristics[Sex]  \
 sample                                                                                                                                                                                                
 RRRM-2_FL_ISS_14   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_16   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_17   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_19   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_20   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_02   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_05   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_07   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_FL_ISS_08   http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_13  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_16  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_18  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_19  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_01  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_02  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_04  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_07  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 RRRM-2_HGC_ISS_09  http://www.ebi.ac.uk/efo/EFO_0020093                 Wild Type              NCIT  http://purl.obolibrary.org/obo/NCIT_C62195            Taconic Biosciences               Female   
 
                   Term Source REF.3                            Term Accession Number.3 Factor Value[Spaceflight] Term Source REF.4                            Term Accession Number.4  \
 sample                                                                                                                                                                                  
 RRRM-2_FL_ISS_14               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_16               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_17               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_19               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_20               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_02               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_05               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_07               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_FL_ISS_08               MESH  http://purl.bioontology.org/ontology/MESH/D005260              Space Flight              MESH  http://purl.bioontology.org/ontology/MESH/D013026   
 RRRM-2_HGC_ISS_13              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_16              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_18              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_19              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_01              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_02              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_04              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_07              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 RRRM-2_HGC_ISS_09              MESH  http://purl.bioontology.org/ontology/MESH/D005260            Ground Control               OSD                             https://osdr.nasa.gov/   
 
                    Factor Value[Age]  Unit Term Source REF.5                    Term Accession Number.5 Characteristics[Material Type] Term Source REF.6               Term Accession Number.6  \
 sample                                                                                                                                                                                           
 RRRM-2_FL_ISS_14                  29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_16                  29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_17                  29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_19                  29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_20                  29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_02                  12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_05                  12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_07                  12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_FL_ISS_08                  12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_13                 29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_16                 29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_18                 29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_19                 29  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_01                 12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_02                 12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_04                 12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_07                 12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 RRRM-2_HGC_ISS_09                 12  week                UO  http://purl.obolibrary.org/obo/UO_0000034       Left cerebral hemisphere               FMA  http://purl.org/sig/ont/fma/fma61819   
 
                                        Characteristics[diet] Characteristics[Feeding Schedule]  Characteristics[Age at Euthanasia] Unit.1 Term Source REF.7  \
 sample                                                                                                                                                        
 RRRM-2_FL_ISS_14   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_FL_ISS_16   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_FL_ISS_17   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_FL_ISS_19   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_FL_ISS_20   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_FL_ISS_02   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_FL_ISS_05   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_FL_ISS_07   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_FL_ISS_08   Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_HGC_ISS_13  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_HGC_ISS_16  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_HGC_ISS_18  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_HGC_ISS_19  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  37   week                UO   
 RRRM-2_HGC_ISS_01  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_HGC_ISS_02  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_HGC_ISS_04  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_HGC_ISS_07  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 RRRM-2_HGC_ISS_09  Nutrient Upgraded Rodent Food Bar (NuRFB)                        ad libitum                                  20   week                UO   
 
                                      Term Accession Number.7      Protocol REF                          Parameter Value[habitat] Parameter Value[Enrichment material]  Parameter Value[duration]  \
 sample                                                                                                                                                                                             
 RRRM-2_FL_ISS_14   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_16   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_17   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_19   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_20   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_02   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_05   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_07   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_FL_ISS_08   http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_13  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_16  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_18  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_19  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_01  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_02  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_04  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_07  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 RRRM-2_HGC_ISS_09  http://purl.obolibrary.org/obo/UO_0000034  Animal Husbandry  Rodent Flight Hardware (Transporter and Habitat)                                  Hut                         53   
 
                   Unit.2 Term Source REF.8                    Term Accession Number.8 Parameter Value[light cycle]     Protocol REF.1                 Parameter Value[Euthanasia Method]  \
 sample                                                                                                                                                                                     
 RRRM-2_FL_ISS_14     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_16     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_17     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_19     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_20     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_02     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_05     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_07     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_FL_ISS_08     day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_13    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_16    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_18    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_19    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_01    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_02    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_04    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_07    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 RRRM-2_HGC_ISS_09    day                UO  http://purl.obolibrary.org/obo/UO_0000033        12 h light/dark cycle  sample collection  Bilateral thoracotomy with sedation, Cardiac p...   
 
                   Parameter Value[Sample Preservation Method] Term Source REF.9                     Term Accession Number.9  Parameter Value[Sample Storage Temperature]          Unit.3  \
 sample                                                                                                                                                                                     
 RRRM-2_FL_ISS_14                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_16                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_17                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_19                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_20                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_02                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_05                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_07                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_FL_ISS_08                                  Cryochiller               OSD                      https://osdr.nasa.gov/                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_13                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_16                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_18                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_19                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_01                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_02                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_04                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_07                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 RRRM-2_HGC_ISS_09                             Liquid Nitrogen              NCIT  http://purl.obolibrary.org/obo/NCIT_C68796                                          -80  degree Celsius   
 
                   Term Source REF.10                   Term Accession Number.10 Comment[RFID] Comment[Euthanasia Date]                      Group  
 sample                                                                                                                                             
 RRRM-2_FL_ISS_14                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E2A180C12              18-Sep-2019    Space Flight | 37 Weeks  
 RRRM-2_FL_ISS_16                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E27313758              19-Sep-2019    Space Flight | 37 Weeks  
 RRRM-2_FL_ISS_17                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E353B735A              19-Sep-2019    Space Flight | 37 Weeks  
 RRRM-2_FL_ISS_19                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E3D2C2D2C              19-Sep-2019    Space Flight | 37 Weeks  
 RRRM-2_FL_ISS_20                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E272D285A              19-Sep-2019    Space Flight | 37 Weeks  
 RRRM-2_FL_ISS_02                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E394B6C23              16-Sep-2019    Space Flight | 20 Weeks  
 RRRM-2_FL_ISS_05                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E3E102A12              16-Sep-2019    Space Flight | 20 Weeks  
 RRRM-2_FL_ISS_07                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E3E325E7C              17-Sep-2019    Space Flight | 20 Weeks  
 RRRM-2_FL_ISS_08                  UO  http://purl.obolibrary.org/obo/UO_0000027    6E3C42091B              17-Sep-2019    Space Flight | 20 Weeks  
 RRRM-2_HGC_ISS_13                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E3A3C1239              19-Sep-2019  Ground Control | 37 Weeks  
 RRRM-2_HGC_ISS_16                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E35413D61              20-Sep-2019  Ground Control | 37 Weeks  
 RRRM-2_HGC_ISS_18                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E3C216122              20-Sep-2019  Ground Control | 37 Weeks  
 RRRM-2_HGC_ISS_19                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E3C342F47              20-Sep-2019  Ground Control | 37 Weeks  
 RRRM-2_HGC_ISS_01                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E28307F36              18-Sep-2019  Ground Control | 20 Weeks  
 RRRM-2_HGC_ISS_02                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E371E2032              18-Sep-2019  Ground Control | 20 Weeks  
 RRRM-2_HGC_ISS_04                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E2A671967              18-Sep-2019  Ground Control | 20 Weeks  
 RRRM-2_HGC_ISS_07                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E28536840              18-Sep-2019  Ground Control | 20 Weeks  
 RRRM-2_HGC_ISS_09                 UO  http://purl.obolibrary.org/obo/UO_0000027    6E3C705065              19-Sep-2019  Ground Control | 20 Weeks  ]

QC¶

Perform Sample-Specific QC¶

In [4]:
%matplotlib inline

plot_qc = False  # change to True to get sample-level QC plots (a bit slow)
qcs, n_cells_by_counts, descriptives, figs = scflow.pp.perform_qc_multi(
    adatas, col_batch=col_batch, col_sample=col_sample, plot=plot_qc,
    percentiles=[0.025, 0.10, 0.25, 0.50, 0.75, 0.85, 0.90, 0.975],
    figsize=(10, 10))  # perform QC on individual samples
for x in qcs:  # iterate QC metrics % plot percentiles by group
    fig = sns.catplot(qcs, y=x, hue=col_batch, kind="violin")
    fig.fig.suptitle(x)
    fig = sns.catplot(descriptives.loc[:, :, x][[
        i for i in descriptives if ("%" in i)]].stack().to_frame("Value"),
                      x="Metric", y="Value", kind="bar",
                      hue=col_batch, height=10)
    fig.fig.suptitle(x)
descriptives.stack().unstack("Variable").round()
Out[4]:
Variable n_cells_by_counts n_genes_by_counts pct_counts_mt total_counts
sample Group Metric
RRRM2_BRN_FLT_ISS-T_OLD_FO14 Space Flight | 37 Weeks count 56748.0 4260.0 4260.0 4260.0
mean 228.0 3040.0 2.0 10679.0
std 548.0 1730.0 3.0 9449.0
min 0.0 5.0 0.0 5.0
2.5% 0.0 719.0 0.0 1129.0
10% 0.0 931.0 0.0 1641.0
25% 0.0 1271.0 0.0 2478.0
50% 3.0 3182.0 1.0 8793.0
75% 109.0 4415.0 2.0 15769.0
85% 440.0 4972.0 3.0 20076.0
90% 802.0 5327.0 4.0 23291.0
97.5% 2114.0 6255.0 10.0 33911.0
max 4252.0 9857.0 46.0 69813.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 Space Flight | 37 Weeks count 56748.0 4863.0 4863.0 4863.0
mean 288.0 3358.0 4.0 11512.0
std 671.0 1890.0 6.0 10039.0
min 0.0 71.0 0.0 139.0
2.5% 0.0 766.0 0.0 1213.0
10% 0.0 983.0 0.0 1710.0
25% 0.0 1396.0 1.0 2727.0
50% 3.0 3551.0 3.0 9776.0
75% 130.0 4796.0 6.0 16856.0
85% 601.0 5431.0 8.0 21349.0
90% 1077.0 5831.0 10.0 24861.0
97.5% 2573.0 6861.0 20.0 36130.0
max 4861.0 9750.0 66.0 97274.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 Space Flight | 37 Weeks count 56748.0 1493.0 1493.0 1493.0
mean 37.0 1423.0 4.0 3043.0
std 106.0 1042.0 4.0 3380.0
min 0.0 12.0 0.0 16.0
2.5% 0.0 316.0 0.0 376.0
10% 0.0 418.0 0.0 546.0
25% 0.0 575.0 1.0 752.0
50% 0.0 990.0 4.0 1382.0
75% 13.0 2215.0 6.0 4899.0
85% 58.0 2678.0 6.0 6604.0
90% 111.0 2918.0 7.0 7543.0
97.5% 367.0 3803.0 12.0 11767.0
max 1490.0 6533.0 49.0 31615.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 Space Flight | 37 Weeks count 56748.0 7265.0 7265.0 7265.0
mean 224.0 1747.0 5.0 4006.0
std 595.0 1337.0 6.0 4217.0
min 0.0 2.0 0.0 2.0
2.5% 0.0 194.0 0.0 257.0
10% 0.0 376.0 0.0 491.0
25% 0.0 642.0 1.0 909.0
50% 2.0 1183.0 3.0 1901.0
75% 83.0 2873.0 7.0 6573.0
85% 390.0 3339.0 10.0 8421.0
90% 723.0 3656.0 13.0 9694.0
97.5% 2138.0 4636.0 21.0 14725.0
max 7235.0 6922.0 43.0 32072.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 Space Flight | 37 Weeks count 56748.0 5641.0 5641.0 5641.0
mean 256.0 2575.0 3.0 6564.0
std 629.0 1523.0 3.0 5498.0
min 0.0 3.0 0.0 4.0
2.5% 0.0 628.0 0.0 895.0
10% 0.0 795.0 0.0 1221.0
25% 0.0 1048.0 1.0 1722.0
50% 2.0 2730.0 2.0 5725.0
75% 104.0 3763.0 4.0 9754.0
85% 498.0 4231.0 6.0 11979.0
90% 907.0 4551.0 8.0 13725.0
97.5% 2384.0 5545.0 13.0 19711.0
max 5629.0 7819.0 29.0 38447.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 Space Flight | 20 Weeks count 56748.0 4125.0 4125.0 4125.0
mean 237.0 3261.0 2.0 11070.0
std 552.0 1915.0 3.0 9862.0
min 0.0 5.0 0.0 5.0
2.5% 0.0 700.0 0.0 1087.0
10% 0.0 971.0 0.0 1667.0
25% 0.0 1326.0 1.0 2493.0
50% 3.0 3534.0 1.0 9574.0
75% 113.0 4776.0 3.0 16538.0
85% 489.0 5352.0 4.0 20849.0
90% 877.0 5761.0 5.0 24583.0
97.5% 2119.0 6848.0 11.0 35338.0
max 4117.0 9338.0 48.0 64091.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 Space Flight | 20 Weeks count 56748.0 4714.0 4714.0 4714.0
mean 279.0 3358.0 4.0 10940.0
std 656.0 1779.0 5.0 9018.0
min 0.0 157.0 0.0 199.0
2.5% 0.0 810.0 0.0 1300.0
10% 0.0 1024.0 1.0 1811.0
25% 0.0 1474.0 1.0 2978.0
50% 3.0 3726.0 2.0 10188.0
75% 127.0 4678.0 5.0 15560.0
85% 566.0 5177.0 7.0 18941.0
90% 1026.0 5521.0 10.0 21725.0
97.5% 2544.0 6603.0 20.0 32232.0
max 4714.0 11910.0 42.0 117852.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 Space Flight | 20 Weeks count 56748.0 4238.0 4238.0 4238.0
mean 128.0 1718.0 3.0 4000.0
std 346.0 1204.0 3.0 3795.0
min 0.0 2.0 0.0 2.0
2.5% 0.0 332.0 0.0 453.0
10% 0.0 427.0 0.0 612.0
25% 0.0 570.0 1.0 864.0
50% 1.0 1644.0 1.0 3057.0
75% 49.0 2567.0 3.0 5973.0
85% 211.0 3028.0 5.0 7670.0
90% 398.0 3371.0 6.0 9119.0
97.5% 1255.0 4271.0 11.0 13453.0
max 4227.0 6450.0 56.0 31424.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 Space Flight | 20 Weeks count 56748.0 5658.0 5658.0 5658.0
mean 299.0 3002.0 2.0 8364.0
std 698.0 1854.0 3.0 7866.0
min 0.0 2.0 0.0 2.0
2.5% 0.0 481.0 0.0 682.0
10% 0.0 904.0 0.0 1381.0
25% 0.0 1177.0 1.0 1971.0
50% 3.0 3180.0 1.0 6891.0
75% 137.0 4413.0 3.0 12232.0
85% 635.0 5013.0 4.0 15381.0
90% 1121.0 5394.0 5.0 17975.0
97.5% 2639.0 6617.0 11.0 27907.0
max 5639.0 10018.0 47.0 81281.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 Ground Control | 37 Weeks count 56748.0 3880.0 3880.0 3880.0
mean 190.0 2777.0 0.0 7470.0
std 462.0 1513.0 1.0 6139.0
min 0.0 49.0 0.0 56.0
2.5% 0.0 823.0 0.0 1283.0
10% 0.0 1035.0 0.0 1752.0
25% 0.0 1308.0 0.0 2338.0
50% 1.0 2736.0 0.0 5855.0
75% 81.0 4017.0 0.0 10922.0
85% 371.0 4484.0 1.0 13579.0
90% 681.0 4839.0 1.0 15959.0
97.5% 1735.0 5684.0 2.0 23157.0
max 3879.0 8613.0 17.0 47275.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 Ground Control | 37 Weeks count 56748.0 20000.0 20000.0 20000.0
mean 374.0 1062.0 2.0 2148.0
std 1228.0 885.0 2.0 2710.0
min 0.0 2.0 0.0 2.0
2.5% 0.0 201.0 0.0 235.0
10% 0.0 327.0 0.0 403.0
25% 0.0 473.0 1.0 626.0
50% 3.0 727.0 1.0 1045.0
75% 114.0 1283.0 2.0 2176.0
85% 492.0 2102.0 3.0 4598.0
90% 954.0 2487.0 4.0 5938.0
97.5% 3744.0 3398.0 6.0 10018.0
max 19922.0 6446.0 46.0 29528.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 Ground Control | 37 Weeks count 56748.0 6503.0 6503.0 6503.0
mean 207.0 1804.0 1.0 4486.0
std 597.0 1025.0 2.0 3585.0
min 0.0 6.0 0.0 6.0
2.5% 0.0 326.0 0.0 422.0
10% 0.0 499.0 0.0 724.0
25% 0.0 852.0 0.0 1406.0
50% 2.0 1859.0 0.0 3932.0
75% 70.0 2525.0 1.0 6535.0
85% 294.0 2865.0 1.0 7913.0
90% 575.0 3103.0 2.0 9028.0
97.5% 2165.0 3933.0 4.0 13468.0
max 6487.0 5740.0 41.0 24455.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 Ground Control | 37 Weeks count 56748.0 8476.0 8476.0 8476.0
mean 427.0 2859.0 1.0 7321.0
std 1039.0 1505.0 2.0 5759.0
min 0.0 2.0 0.0 2.0
2.5% 0.0 666.0 0.0 942.0
10% 0.0 846.0 0.0 1274.0
25% 0.0 1285.0 0.0 2102.0
50% 3.0 3122.0 1.0 6965.0
75% 177.0 3886.0 1.0 10141.0
85% 843.0 4325.0 2.0 12228.0
90% 1533.0 4691.0 3.0 14150.0
97.5% 3946.0 5748.0 6.0 21190.0
max 8456.0 11828.0 37.0 89715.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 Ground Control | 20 Weeks count 56748.0 3968.0 3968.0 3968.0
mean 246.0 3514.0 1.0 12739.0
std 573.0 1800.0 1.0 10626.0
min 0.0 13.0 0.0 16.0
2.5% 0.0 923.0 0.0 1490.0
10% 0.0 1189.0 0.0 2164.0
25% 0.0 1674.0 0.0 3348.0
50% 3.0 3762.0 0.0 11094.0
75% 126.0 4900.0 1.0 18537.0
85% 498.0 5415.0 1.0 23128.0
90% 894.0 5754.0 2.0 26395.0
97.5% 2231.0 6747.0 4.0 37457.0
max 3960.0 10980.0 27.0 90669.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 Ground Control | 20 Weeks count 56748.0 10779.0 10779.0 10779.0
mean 479.0 2524.0 1.0 7477.0
std 1214.0 1550.0 2.0 7294.0
min 0.0 6.0 0.0 6.0
2.5% 0.0 507.0 0.0 696.0
10% 0.0 703.0 0.0 1065.0
25% 0.0 974.0 0.0 1611.0
50% 4.0 2605.0 0.0 6040.0
75% 203.0 3744.0 1.0 11368.0
85% 850.0 4232.0 2.0 14304.0
90% 1585.0 4526.0 2.0 16462.0
97.5% 4700.0 5465.0 5.0 23663.0
max 10758.0 14427.0 39.0 279678.0
RRRM2_BRN_GC_ISS-T_YNG_GY4 Ground Control | 20 Weeks count 56748.0 8542.0 8542.0 8542.0
mean 241.0 1602.0 1.0 3563.0
std 687.0 970.0 2.0 3047.0
min 0.0 3.0 0.0 3.0
2.5% 0.0 321.0 0.0 414.0
10% 0.0 472.0 0.0 662.0
25% 0.0 726.0 0.0 1099.0
50% 2.0 1492.0 1.0 2706.0
75% 88.0 2303.0 1.0 5259.0
85% 363.0 2624.0 2.0 6457.0
90% 693.0 2882.0 3.0 7411.0
97.5% 2448.0 3657.0 6.0 11122.0
max 8521.0 8455.0 26.0 40300.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 Ground Control | 20 Weeks count 56748.0 5776.0 5776.0 5776.0
mean 304.0 2989.0 0.0 8828.0
std 730.0 1660.0 1.0 7667.0
min 0.0 8.0 0.0 8.0
2.5% 0.0 799.0 0.0 1176.0
10% 0.0 998.0 0.0 1596.0
25% 0.0 1315.0 0.0 2276.0
50% 3.0 3176.0 0.0 7404.0
75% 136.0 4256.0 0.0 12784.0
85% 606.0 4764.0 1.0 16101.0
90% 1099.0 5158.0 1.0 18690.0
97.5% 2806.0 6192.0 3.0 28050.0
max 5768.0 9784.0 30.0 61453.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 Ground Control | 20 Weeks count 56748.0 6044.0 6044.0 6044.0
mean 310.0 2914.0 1.0 7814.0
std 753.0 1538.0 2.0 6297.0
min 0.0 6.0 0.0 6.0
2.5% 0.0 703.0 0.0 1006.0
10% 0.0 910.0 0.0 1404.0
25% 0.0 1368.0 0.0 2293.0
50% 3.0 3133.0 1.0 7126.0
75% 132.0 3980.0 1.0 10850.0
85% 609.0 4465.0 2.0 13366.0
90% 1111.0 4814.0 2.0 15532.0
97.5% 2884.0 5959.0 5.0 23784.0
max 6028.0 10304.0 36.0 73153.0
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Auto-Detect Filtering Thresholds¶

Use 2.5th and/& 97.5th percentile (sample-specific) as minimum genes per cell and minimum and maximum total counts (subject to specified absolute minima). Use 97.5th percentile as upper bound for percent mitochondrial count.

Use an absolute minimum cells per gene.

Also include arguments to run a PCA on individual samples before integrating.

In [5]:
# Options
bounds = descriptives[["2.5%", "97.5%"]].apply(lambda x: list(
    x), axis=1).unstack("Variable")  # list top/bottom 5% (~sample, variable)
abs_min_cells = 20  # regardless of %ile, minimum cells to retain gene
abs_min_genes = 200  # regardless of %ile, minimum genes to retain cell
abs_min_count = 300  # regardless of %ile, minimum counts to retain cell
abs_max_mt = 10  # regardless of %ile, absolute maximum mitochonrial content
n_top_genes = 2000  # number of top genes to count as HVGs

# Set Thresholds
kws_pp = {}
for x in adatas:
    b_x = bounds.loc[x]
    b_counts =  b_x["total_counts"] if isinstance(b_x[
            "total_counts"], list) else b_x["total_counts"].iloc[0]
    b_counts = [max(b_counts[0], abs_min_count), b_counts[1]]
    kws_pp[x] = {
        "min_max_genes": [max((b_x["n_genes_by_counts"] if isinstance(
            b_x["n_genes_by_counts"], list) else b_x[
                "n_genes_by_counts"].iloc[0])[0], abs_min_genes), None],
        "min_max_cells": [abs_min_cells, None],
        # "min_max_cells": [max((b_x["n_cells_by_counts"] if isinstance(
        #     b_x["n_genes_by_counts"], list) else b_x[
        #         "n_genes_by_counts"].iloc[0])[0], abs_min_cells), None],
        # "max_mt": min(abs_max_mt, (b_x["pct_counts_mt"] if isinstance(b_x[
        #     "pct_counts_mt"], list) else b_x["pct_counts_mt"].iloc[0])[1]),
        "max_mt": abs_max_mt,
        "min_max_counts": b_counts,
        # "vars_regress_out": ["total_counts"],
        "target_sum": 1e6,
        "zero_center": True, "max_value": 10,  # scaling
        "n_top_genes": n_top_genes,
        "doublet_detection": "drop"
    }
print("\n".join([f"{s}: {kws_pp[s]}" for s in kws_pp]))
pd.DataFrame(kws_pp).T

RRRM2_BRN_GC_ISS-T_YNG_GY4: {'min_max_genes': [321.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [414.0, 11122.175000000005], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_YNG_GY9: {'min_max_genes': [703.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1006.1500000000001, 23784.40000000001], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_OLD_GO18: {'min_max_genes': [326.1, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [422.1, 13468.399999999998], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_OLD_FO20: {'min_max_genes': [628.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [895.0, 19711.0], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_OLD_GO19: {'min_max_genes': [666.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [942.0, 21190.0], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_OLD_GO13: {'min_max_genes': [822.975, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1282.925, 23156.550000000007], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_YNG_FY8: {'min_max_genes': [481.425, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [681.7, 27907.149999999994], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_YNG_FY7: {'min_max_genes': [331.925, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [452.77500000000003, 13453.3], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_OLD_FO19: {'min_max_genes': [200, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 14724.999999999998], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_YNG_GY7: {'min_max_genes': [798.75, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1176.375, 28050.25], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_OLD_FO14: {'min_max_genes': [719.475, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1128.9, 33910.62499999997], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_YNG_GY1: {'min_max_genes': [923.4000000000001, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1489.7, 37456.77499999999], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_YNG_FY2: {'min_max_genes': [700.2, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1087.2, 35338.0], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_OLD_FO17: {'min_max_genes': [316.3, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [376.3, 11766.900000000009], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_OLD_GO16: {'min_max_genes': [201.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [300, 10018.199999999983], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_OLD_FO16: {'min_max_genes': [766.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1213.2, 36130.25], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_GC_ISS-T_YNG_GY2: {'min_max_genes': [507.0, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [696.45, 23662.899999999972], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
RRRM2_BRN_FLT_ISS-T_YNG_FY5: {'min_max_genes': [809.825, None], 'min_max_cells': [20, None], 'max_mt': 10, 'min_max_counts': [1300.3, 32231.825000000015], 'target_sum': 1000000.0, 'zero_center': True, 'max_value': 10, 'n_top_genes': 2000, 'doublet_detection': 'drop'}
Out[5]:
min_max_genes min_max_cells max_mt min_max_counts target_sum zero_center max_value n_top_genes doublet_detection
RRRM2_BRN_GC_ISS-T_YNG_GY4 [321.0, None] [20, None] 10 [414.0, 11122.175000000005] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_YNG_GY9 [703.0, None] [20, None] 10 [1006.1500000000001, 23784.40000000001] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_OLD_GO18 [326.1, None] [20, None] 10 [422.1, 13468.399999999998] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_OLD_FO20 [628.0, None] [20, None] 10 [895.0, 19711.0] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_OLD_GO19 [666.0, None] [20, None] 10 [942.0, 21190.0] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_OLD_GO13 [822.975, None] [20, None] 10 [1282.925, 23156.550000000007] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_YNG_FY8 [481.425, None] [20, None] 10 [681.7, 27907.149999999994] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_YNG_FY7 [331.925, None] [20, None] 10 [452.77500000000003, 13453.3] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_OLD_FO19 [200, None] [20, None] 10 [300, 14724.999999999998] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_YNG_GY7 [798.75, None] [20, None] 10 [1176.375, 28050.25] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_OLD_FO14 [719.475, None] [20, None] 10 [1128.9, 33910.62499999997] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_YNG_GY1 [923.4000000000001, None] [20, None] 10 [1489.7, 37456.77499999999] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_YNG_FY2 [700.2, None] [20, None] 10 [1087.2, 35338.0] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_OLD_FO17 [316.3, None] [20, None] 10 [376.3, 11766.900000000009] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_OLD_GO16 [201.0, None] [20, None] 10 [300, 10018.199999999983] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_OLD_FO16 [766.0, None] [20, None] 10 [1213.2, 36130.25] 1000000.0 True 10 2000 drop
RRRM2_BRN_GC_ISS-T_YNG_GY2 [507.0, None] [20, None] 10 [696.45, 23662.899999999972] 1000000.0 True 10 2000 drop
RRRM2_BRN_FLT_ISS-T_YNG_FY5 [809.825, None] [20, None] 10 [1300.3, 32231.825000000015] 1000000.0 True 10 2000 drop

Integrate¶

In-Memory Approach¶

See the "On-Disk Approach" for a more sophisticated approach to defining kws_integrate (more options).

In [6]:
# %%time

# # Integrate
# join_method = "outer"  # or "inner"
# kws_integrate = {"kws_pp": kws_pp, "n_top_genes": 10000,
#                  "fill_value": np.nan,
#                  "col_batch": None,  # suppress using batch as covariate
#                  "join": join_method, "merge": "unique", "use_rapids": True}
# self = scflow.Rna(adatas, col_sample=col_sample, col_batch=col_batch,
#                   kws_integrate=kws_integrate)
# self.rna.obs = self.rna.obs.assign(kws_integrate=str(kws_integrate))

# # Write Files for Processed/Integrated Objects?
# if overwrite is True or not os.path.exists(file_new):
#     os.makedirs("data", exist_ok=True)
#     self.rna.write_h5ad(file_new)
# for x in adatas:
#     pfp = os.path.join("data", f"{x}_processed.h5ad")
#     if overwrite is True or not os.path.exists(pfp):
#         adatas[x].write_h5ad(pfp)
# del adatas  # to save memory; now integrated in `self.rna`

# # Display
# print(self.rna)
# # print(self.rna.var.head())
# self.rna.obs

On-Disk Approach¶

Preprocess Individual¶

In [ ]:
# Preprocess
if overwrite is False and os.path.exists(pfp):
    raise ValueError("Must be able to overwrite to use on-disk option")
files_individual = dict(zip(files.keys(), [os.path.join(
    "data", f"{x}_processed.h5ad") for x in files]))  # new individual files
var_names = []  # to store genes not filtered out for each sample
for x in files:  # iterate sample files
    print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}")
    adatas[x].obs.loc[:, f"kws_pp_{col_sample}"] = str(kws_pp[x])  # store kws
    adatas[x] = scflow.pp.preprocess(
        adatas[x], **kws_pp[x], plot_qc=False)  # preprocess data
    var_names += [set(adatas[x].var_names)]  # track what genes still present

# Decide Join Method
shared_genes = set.intersection(*var_names)  # genes in all after filtering
all_genes = set.union(*var_names)  # genes in any post-filter sample
print(f"{len(shared_genes)} genes present in all samples post-filtering (out"
      f" of {len(all_genes)} total genes present in any post-filter sample)")
join_method = "inner" if len(shared_genes) / len(all_genes) >= 0.5 else \
    "outer"  # outer join if <1/2 of genes shared across all samples


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY4
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 8542 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 14 cells that have less than 321.0 genes expressed
	***Filtering genes by cells...
filtered out 37392 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.15
Detected doublet rate = 5.9%
Estimated detectable doublet fraction = 60.3%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 9.8%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY9
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 6044 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 15 cells that have less than 703.0 genes expressed
	***Filtering genes by cells...
filtered out 36336 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.19
Detected doublet rate = 4.0%
Estimated detectable doublet fraction = 55.4%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 7.2%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO18
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 6503 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 13 cells that have less than 326.1 genes expressed
	***Filtering genes by cells...
filtered out 38318 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.16
Detected doublet rate = 4.8%
Estimated detectable doublet fraction = 55.9%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 8.5%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO20
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 5641 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 11 cells that have less than 628.0 genes expressed
	***Filtering genes by cells...
filtered out 37308 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.20
Detected doublet rate = 2.8%
Estimated detectable doublet fraction = 54.3%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 5.2%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO19
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 8476 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 23 cells that have less than 666.0 genes expressed
	***Filtering genes by cells...
filtered out 35414 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.15
Detected doublet rate = 5.6%
Estimated detectable doublet fraction = 66.2%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 8.5%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO13
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 3880 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 10 cells that have less than 822.975 genes expressed
	***Filtering genes by cells...
filtered out 38134 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.22
Detected doublet rate = 2.1%
Estimated detectable doublet fraction = 52.7%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 4.0%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY8
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 5658 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 5 cells that have less than 481.425 genes expressed
	***Filtering genes by cells...
filtered out 36367 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.18
Detected doublet rate = 3.2%
Estimated detectable doublet fraction = 56.2%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 5.7%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY7
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 4238 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 10 cells that have less than 331.925 genes expressed
	***Filtering genes by cells...
filtered out 39609 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.14
Detected doublet rate = 5.8%
Estimated detectable doublet fraction = 59.9%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 9.7%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO19
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 7265 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 2 cells that have less than 200 genes expressed
	***Filtering genes by cells...
filtered out 38066 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.24
Detected doublet rate = 2.3%
Estimated detectable doublet fraction = 28.4%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 8.2%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY7
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 5776 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 18 cells that have less than 798.75 genes expressed
	***Filtering genes by cells...
filtered out 36306 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.18
Detected doublet rate = 3.5%
Estimated detectable doublet fraction = 55.2%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 6.4%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO14
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 4260 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 13 cells that have less than 719.475 genes expressed
	***Filtering genes by cells...
filtered out 36635 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.22
Detected doublet rate = 1.8%
Estimated detectable doublet fraction = 47.7%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 3.8%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY1
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 3968 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 5 cells that have less than 923.4000000000001 genes expressed
	***Filtering genes by cells...
filtered out 36091 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.21
Detected doublet rate = 2.1%
Estimated detectable doublet fraction = 43.2%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 4.9%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY2
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 4125 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 8 cells that have less than 700.2 genes expressed
	***Filtering genes by cells...
filtered out 36646 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.21
Detected doublet rate = 1.9%
Estimated detectable doublet fraction = 46.8%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 4.0%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO17
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 1493 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 8 cells that have less than 316.3 genes expressed
	***Filtering genes by cells...
filtered out 44446 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.20
Detected doublet rate = 2.9%
Estimated detectable doublet fraction = 30.6%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 9.4%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO16
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 20000 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
	***Filtering genes by cells...
filtered out 36883 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.43
Detected doublet rate = 0.3%
Estimated detectable doublet fraction = 7.4%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 4.2%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO16
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 4863 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 14 cells that have less than 766.0 genes expressed
	***Filtering genes by cells...
filtered out 36357 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.21
Detected doublet rate = 2.3%
Estimated detectable doublet fraction = 43.5%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 5.3%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY2
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 10779 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 14 cells that have less than 507.0 genes expressed
	***Filtering genes by cells...
filtered out 34632 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.17
Detected doublet rate = 5.1%
Estimated detectable doublet fraction = 50.9%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 9.9%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY5
================================================================================
	***Activating layer 'counts'...
AnnData object with n_obs × n_vars = 4714 × 56748
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    layers: 'counts'
	***Filtering cells by counts...
	***Filtering cells by genes...
filtered out 21 cells that have less than 809.825 genes expressed
	***Filtering genes by cells...
filtered out 36239 genes that are detected in less than 20 cells
	***Filtering cells by mitochondrial gene content...
	***Performing doublet detection...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/cuml/internals/api_decorators.py:216: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
  ret = func(*args, **kwargs)

Automatically set threshold at doublet score = 0.18
Detected doublet rate = 3.1%
Estimated detectable doublet fraction = 54.1%
Overall doublet rate:
	Expected   = 5.0%
	Estimated  = 5.7%
	***Normalizing...
	***Detecting highly variable genes...
	***Scaling data...
12209 genes present in all samples post-filtering (out of 23329 total genes present in any post-filter sample)

Cluster Individual¶

Generalizable¶

In [8]:
# %%time

# # Clustering Parameter Options (Only Applied If `kws_cluster` != None)
# resolution_individual, min_dist_individual = 0.15, 1.5
# col_cluster_individual = "annotation_by_markers_individual"
# unlabeled_cat = "Heterogeneous"  # if can't find one best-fit cell label
# cci_scanvi = col_cluster_individual + "_heterogeneous_collapsed"
# sep = " | "  # separator for heterogeneous annotations

# # Preprocessing & (Optionally) Clustering
# if overwrite is False and os.path.exists(pfp):
#     raise ValueError("Must be able to overwrite to use on-disk option")
# # del adatas
# for x in files:  # iterate sample files
#     if kws_cluster is not None:  # cluster individual sample?
#         kws_cl = {"resolution": resolution_individual[x],
#                   "min_dist": min_dist_individual[x], **kws_cluster}
#         self.rna.obs.loc[:, "kws_cluster_individual"] = str(kws_cl)
#         adatas[x] = scflow.pp.cluster(
#             adatas[x], resolution=resolution_individual,
#             min_dist=min_dist_individual, plot=False,
#             col_celltype="leiden_individual", **kws_cl)  # cluster
#         sc.tl.rank_genes_groups(
#             adatas[x], "leiden_individual", n_genes=None, rankby_abs=False,
#             key_added="rank_genes_groups_leiden_individual",
#             copy=False)  # find markers/DEGs
#         if markers_predefined is not None:  # markers specified?
#             _, adatas[x] = scflow.pp.annotate_by_marker_overlap(
#                 adatas[x], markers_predefined,
#                 col_celltype="leiden_individual",
#                 col_celltype_new=col_cluster_individual, sep=sep,
#                 celltypes_superhierarchical=cts_superhierarchical,
#                 # top_n_markers=20,
#                 adj_pval_threshold=1e-10,
#                 method="overlap_coef", inplace=True)  # annotate by markers
#             if rename_marker_based_annotation is not None:
#                 adatas[x].obs.loc[:, col_cluster_individual] = adatas[
#                     x].obs[col_cluster_individual].replace(
#                         rename_marker_based_annotation)  # re-name
#             adatas[x].obs.loc[:, cci_scanvi] = adatas[x].obs[
#                 col_cluster_individual].apply(lambda x: unlabeled_cat if (
#                     sep in x) else x)  # re-label heterogeneous annotations
#     var_names += [set(adatas[x].var_names)]  # track what genes still there

# # Write Objects
# for x in adatas:
#     print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}\n\n{adatas[x]}\n")
#     adatas[x].write_h5ad(files_individual[x])  # write individual file

# # Plot
# if kws_cluster is not None:
#     for x in files:  # iterate sample files
#         ccs = [v for v in ["leiden_individual", col_cluster_individual
#                            ] if v in adatas[x].obs]  # columns for UMAP
#         print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}")
#         sc.pl.umap(adatas[x], color=ccs, wspace=0.4)  # plot UMAP
# del adatas  # save memory

OSD-613-Specific Tweaks¶

Iterate different clustering parameters to ensure extraction of common cell types

Make sample-specific alterations to one sample's annotation

In [ ]:
# Clustering Parameter Options (Only Applied If `kws_cluster` != None)
req_cts = ["Neuron", "OPC", "Oligodendrocyte", "Astrocyte", "Microglial"]
prohib_cts = ["Excitatory-Inhibitory"]
resolution_individual, min_dist_individual = 0.2, 1.5
resn_list = [0.2, 0.5, 0.1, 0.075, 0.15]
dist_list = [1, 0.75, 0.5, 1.5, 0.3]
col_cluster_individual = "annotation_by_markers_individual"
unlabeled_cat = "Heterogeneous"  # if can't find one best-fit cell label
cci_scanvi = col_cluster_individual + "_heterogeneous_collapsed"
sep = " | "  # separator for heterogeneous annotations

# Clustering
for x in files:  # iterate sample files
    valid_cts = False
    print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}")
    for r in resn_list:
        if valid_cts is True:
            break
        for t in dist_list:
            if valid_cts is True:
                break
            c_i = f"leiden_individual_res{r}dist{t}"
            cai = f"{col_cluster_individual}_res{r}dist{t}"
            kws_cl = {"resolution": r, "min_dist": t}
            adatas[x] = scflow.pp.cluster(
                adatas[x], plot=False, col_celltype=c_i, **kws_cl)
            sc.tl.rank_genes_groups(
                adatas[x], c_i, n_genes=None,
                key_added=f"rank_genes_groups_{c_i}",
                copy=False)  # find markers/DEGs
            _, adatas[x] = scflow.pp.annotate_by_marker_overlap(
                adatas[x], mks_collapsed,
                col_celltype=c_i, col_celltype_new=cai, sep=sep,
                celltypes_superhierarchical=cts_superhierarchical,
                # top_n_markers=20,
                adj_pval_threshold=1e-15,
                method="overlap_count", inplace=True)  # annotate
            if rename_marker_based_annotation is not None:
                adatas[x].obs.loc[:, cai] = adatas[x].obs[cai].replace(
                        rename_marker_based_annotation)  # re-name
            print(adatas[x].obs[cai].unique())
            adatas[x].obs.loc[:, cci_scanvi] = adatas[x].obs[
                cai].apply(lambda x: unlabeled_cat if (sep in x) else x)
            valid_cts = all([q in adatas[x].obs[cai].unique()
                             for q in req_cts])
            valid_cts = valid_cts and (all([q not in adatas[x].obs[
                cai].unique() for q in prohib_cts]))
            valid_cts = False if any(adatas[x].obs[
                cci_scanvi] == unlabeled_cat) else valid_cts
            if valid_cts is True:
                adatas[x].obs.loc[:, "leiden_individual"] = adatas[x].obs[c_i]
                adatas[x].obs.loc[:, col_cluster_individual] = adatas[
                    x].obs[cai]
                adatas[x].obs.loc[:, "kws_cluster_individual"] = str(kws_cl)
                print(kws_cl)

# Detect Samples with No Valid Clustering Scheme
no_valid = []
for x in files:
    if all((i in adatas[x].obs for i in [
            "leiden_individual", col_cluster_individual])) is False:
        print(f"No valid clustering found for {x}")
        no_valid += [x]
print(f"No Valid: {no_valid}")

# Individual Tweak
samp = "RRRM2_BRN_GC_ISS-T_YNG_GY4"
cols_tries = [i for i in adatas[samp].obs if "leiden_" in i]
cts_try = cols_tries[14]
_, adatas[samp] = scflow.pp.annotate_by_marker_overlap(
    adatas[samp], mks_collapsed, col_celltype=cts_try,
    col_celltype_new=col_cluster_individual + "_new", sep=sep,
    adj_pval_threshold=1e-5,
    method="overlap_coef", inplace=True)  # annotate by markers
adatas[samp].obs.loc[:, "leiden_individual"] = adatas[samp].obs[cts_try]
adatas[samp].obs.loc[:, col_cluster_individual] = adatas[samp].obs[
    col_cluster_individual + "_new"]
adatas[samp].obs["kws_cluster_individual"] = str({"resolution": float(
    cts_try.split("res")[1].split("dist")[0]), "min_dist": float(
        cts_try.split("res")[1].split("dist")[1])})

# Write Objects
for x in adatas:
    print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}\n\n{adatas[x]}\n")
    adatas[x].write_h5ad(files_individual[x])  # write individual file

# Print Parameters Used
for x in adatas:
    print(adatas[x].obs["kws_cluster_individual"])

# Plot
for x in adatas:
    sc.pl.umap(adatas[x], color=[
        "leiden_individual", col_cluster_individual], wspace=0.4)
del adatas


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY4
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...

2025-08-27 12:30:21 | [INFO] init

['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte', 'Microglial']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Neuron | Endothelial', 'Oligodendrocyte', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'OPC', 'Oligodendrocyte', 'Astrocyte', 'Microglial']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'OPC', 'Oligodendrocyte', 'Astrocyte', 'Microglial']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Microglial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Microglial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Microglial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Microglial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Microglial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Neuron | Endothelial', 'Oligodendrocyte', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Neuron | Endothelial', 'Oligodendrocyte', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte', 'Microglial', 'OPC']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'Astrocyte', 'Microglial', 'OPC']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Microglial', 'Oligodendrocyte', 'OPC', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Microglial', 'OPC', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'OPC', 'Neuron | Endothelial', 'Oligodendrocyte', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'OPC', 'Oligodendrocyte', 'Microglial', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'Oligodendrocyte', 'OPC', 'Astrocyte', 'Microglial']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Endothelial', 'Astrocyte', 'Neuron | Endothelial']
Categories (7, object): ['Neuron', 'Endothelial', 'Neuron | Endothelial', 'OPC', 'Oligodendrocyte', 'Astrocyte', 'Microglial']


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY9
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Microglial', 'Oligodendrocyte', 'OPC', 'Astrocyte', 'Endothelial']
Categories (6, object): ['Neuron', 'Endothelial', 'Microglial', 'Oligodendrocyte', 'Astrocyte', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO18
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial', 'Endothelial', 'OPC']
Categories (6, object): ['Neuron', 'Endothelial', 'Microglial', 'Oligodendrocyte', 'Astrocyte', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO20
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'OPC', 'Astrocyte', 'Endothelial']
Categories (6, object): ['Neuron', 'Endothelial', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Microglial']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO19
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.5...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.1...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Astrocyte', 'Endothelial', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'OPC', 'Astrocyte', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Astrocyte', 'Endothelial', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1.5...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'OPC', 'Oligodendrocyte', 'Astrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.3...
	***Performing Leiden clustering with resolution 0.075...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'OPC', 'Astrocyte', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
Categories (5, object): ['Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Oligodendrocyte']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.5...
	***Performing Leiden clustering with resolution 0.15...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial', 'Microglial']
Categories (6, object): ['Neuron', 'Astrocyte', 'OPC', 'Microglial', 'Oligodendrocyte', 'Endothelial']
{'resolution': 0.15, 'min_dist': 0.5}


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO13
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Neuron', 'Endothelial', 'Astrocyte', 'OPC', 'Microglial']
Categories (6, object): ['Neuron', 'Astrocyte', 'Endothelial', 'Oligodendrocyte', 'OPC', 'Microglial']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY8
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'Astrocyte', 'OPC']
Categories (5, object): ['Neuron', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Microglial']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY7
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial', 'OPC', 'Endothelial']
Categories (6, object): ['Neuron', 'Microglial', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Endothelial']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO19
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Endothelial', 'Astrocyte', 'Oligodendrocyte', 'Microglial', 'OPC']
Categories (6, object): ['Endothelial', 'Oligodendrocyte', 'Neuron', 'Microglial', 'Astrocyte', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY7
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...

/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:456: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "names"] = self.var_names[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:458: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "scores"] = scores[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:461: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "pvals"] = pvals[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:471: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "pvals_adj"] = pvals_adj[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:482: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "logfoldchanges"] = np.log2(
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:456: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "names"] = self.var_names[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:458: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "scores"] = scores[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:461: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "pvals"] = pvals[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:471: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "pvals_adj"] = pvals_adj[global_indices]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_rank_genes_groups.py:482: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  self.stats[group_name, "logfoldchanges"] = np.log2(

['Neuron', 'Oligodendrocyte', 'Microglial', 'Astrocyte', 'OPC', 'Endothelial']
Categories (6, object): ['Neuron', 'Astrocyte', 'Endothelial', 'Oligodendrocyte', 'Microglial', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO14
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Microglial', 'Endothelial']
Categories (6, object): ['Neuron', 'Microglial', 'Endothelial', 'Oligodendrocyte', 'Astrocyte', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY1
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Neuron', 'Microglial', 'Astrocyte', 'OPC', 'Endothelial']
Categories (6, object): ['Neuron', 'Endothelial', 'Microglial', 'OPC', 'Oligodendrocyte', 'Astrocyte']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY2
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Oligodendrocyte', 'Neuron', 'Microglial', 'Astrocyte', 'OPC']
Categories (5, object): ['Neuron', 'Astrocyte', 'Oligodendrocyte', 'Microglial', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO17
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Microglial', 'Oligodendrocyte', 'OPC', 'Astrocyte']
Categories (5, object): ['Neuron', 'Microglial', 'Astrocyte', 'Oligodendrocyte', 'OPC']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO16
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Endothelial', 'Neuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial']
Categories (5, object): ['Endothelial', 'Neuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial']
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 0.75...
	***Performing Leiden clustering with resolution 0.2...
['Endothelial', 'OPC', 'Neuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial']
Categories (6, object): ['Endothelial', 'Neuron', 'Oligodendrocyte', 'Astrocyte', 'Microglial', 'OPC']
{'resolution': 0.2, 'min_dist': 0.75}


================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO16
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Microglial', 'Astrocyte', 'OPC']
Categories (5, object): ['Neuron', 'Astrocyte', 'OPC', 'Oligodendrocyte', 'Microglial']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY2
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Astrocyte', 'Microglial', 'Neuron', 'OPC', 'Oligodendrocyte', 'Endothelial']
Categories (6, object): ['Neuron', 'Microglial', 'OPC', 'Astrocyte', 'Oligodendrocyte', 'Endothelial']
{'resolution': 0.2, 'min_dist': 1}


================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY5
================================================================================
	***Calculating PCA with None components...
	***Building neighborhood...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.2...
['Neuron', 'Oligodendrocyte', 'Astrocyte', 'OPC', 'Microglial', 'Endothelial']
Categories (6, object): ['Neuron', 'Endothelial', 'OPC', 'Astrocyte', 'Oligodendrocyte', 'Microglial']
{'resolution': 0.2, 'min_dist': 1}
No valid clustering found for RRRM2_BRN_GC_ISS-T_YNG_GY4
No Valid: ['RRRM2_BRN_GC_ISS-T_YNG_GY4']
No description has been provided for this image 

================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY4
================================================================================

AnnData object with n_obs × n_vars = 7547 × 19356
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual_res0.2dist0.75', 'annotation_by_markers_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'annotation_by_markers_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.3', 'annotation_by_markers_individual_res0.2dist0.3', 'leiden_individual_res0.5dist1', 'annotation_by_markers_individual_res0.5dist1', 'leiden_individual_res0.5dist0.75', 'annotation_by_markers_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'annotation_by_markers_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1.5', 'annotation_by_markers_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.3', 'annotation_by_markers_individual_res0.5dist0.3', 'leiden_individual_res0.1dist1', 'annotation_by_markers_individual_res0.1dist1', 'leiden_individual_res0.1dist0.75', 'annotation_by_markers_individual_res0.1dist0.75', 'leiden_individual_res0.1dist0.5', 'annotation_by_markers_individual_res0.1dist0.5', 'leiden_individual_res0.1dist1.5', 'annotation_by_markers_individual_res0.1dist1.5', 'leiden_individual_res0.1dist0.3', 'annotation_by_markers_individual_res0.1dist0.3', 'leiden_individual_res0.075dist1', 'annotation_by_markers_individual_res0.075dist1', 'leiden_individual_res0.075dist0.75', 'annotation_by_markers_individual_res0.075dist0.75', 'leiden_individual_res0.075dist0.5', 'annotation_by_markers_individual_res0.075dist0.5', 'leiden_individual_res0.075dist1.5', 'annotation_by_markers_individual_res0.075dist1.5', 'leiden_individual_res0.075dist0.3', 'annotation_by_markers_individual_res0.075dist0.3', 'leiden_individual_res0.15dist1', 'annotation_by_markers_individual_res0.15dist1', 'leiden_individual_res0.15dist0.75', 'annotation_by_markers_individual_res0.15dist0.75', 'leiden_individual_res0.15dist0.5', 'annotation_by_markers_individual_res0.15dist0.5', 'leiden_individual_res0.15dist1.5', 'annotation_by_markers_individual_res0.15dist1.5', 'leiden_individual_res0.15dist0.3', 'annotation_by_markers_individual_res0.15dist0.3', 'annotation_by_markers_individual_new', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1', 'leiden_individual_res0.2dist0.75', 'rank_genes_groups_leiden_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'rank_genes_groups_leiden_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.3', 'rank_genes_groups_leiden_individual_res0.2dist0.3', 'leiden_individual_res0.5dist1', 'rank_genes_groups_leiden_individual_res0.5dist1', 'leiden_individual_res0.5dist0.75', 'rank_genes_groups_leiden_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'rank_genes_groups_leiden_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1.5', 'rank_genes_groups_leiden_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.3', 'rank_genes_groups_leiden_individual_res0.5dist0.3', 'leiden_individual_res0.1dist1', 'rank_genes_groups_leiden_individual_res0.1dist1', 'leiden_individual_res0.1dist0.75', 'rank_genes_groups_leiden_individual_res0.1dist0.75', 'leiden_individual_res0.1dist0.5', 'rank_genes_groups_leiden_individual_res0.1dist0.5', 'leiden_individual_res0.1dist1.5', 'rank_genes_groups_leiden_individual_res0.1dist1.5', 'leiden_individual_res0.1dist0.3', 'rank_genes_groups_leiden_individual_res0.1dist0.3', 'leiden_individual_res0.075dist1', 'rank_genes_groups_leiden_individual_res0.075dist1', 'leiden_individual_res0.075dist0.75', 'rank_genes_groups_leiden_individual_res0.075dist0.75', 'leiden_individual_res0.075dist0.5', 'rank_genes_groups_leiden_individual_res0.075dist0.5', 'leiden_individual_res0.075dist1.5', 'rank_genes_groups_leiden_individual_res0.075dist1.5', 'leiden_individual_res0.075dist0.3', 'rank_genes_groups_leiden_individual_res0.075dist0.3', 'leiden_individual_res0.15dist1', 'rank_genes_groups_leiden_individual_res0.15dist1', 'leiden_individual_res0.15dist0.75', 'rank_genes_groups_leiden_individual_res0.15dist0.75', 'leiden_individual_res0.15dist0.5', 'rank_genes_groups_leiden_individual_res0.15dist0.5', 'leiden_individual_res0.15dist1.5', 'rank_genes_groups_leiden_individual_res0.15dist1.5', 'leiden_individual_res0.15dist0.3', 'rank_genes_groups_leiden_individual_res0.15dist0.3', 'annotation_by_markers_individual_new_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY9
================================================================================

AnnData object with n_obs × n_vars = 5465 × 20412
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO18
================================================================================

AnnData object with n_obs × n_vars = 5842 × 18430
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO20
================================================================================

AnnData object with n_obs × n_vars = 4922 × 19440
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO19
================================================================================

AnnData object with n_obs × n_vars = 7539 × 21334
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual_res0.2dist0.75', 'annotation_by_markers_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'annotation_by_markers_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1.5', 'annotation_by_markers_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.3', 'annotation_by_markers_individual_res0.2dist0.3', 'leiden_individual_res0.5dist1', 'annotation_by_markers_individual_res0.5dist1', 'leiden_individual_res0.5dist0.75', 'annotation_by_markers_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'annotation_by_markers_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1.5', 'annotation_by_markers_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.3', 'annotation_by_markers_individual_res0.5dist0.3', 'leiden_individual_res0.1dist1', 'annotation_by_markers_individual_res0.1dist1', 'leiden_individual_res0.1dist0.75', 'annotation_by_markers_individual_res0.1dist0.75', 'leiden_individual_res0.1dist0.5', 'annotation_by_markers_individual_res0.1dist0.5', 'leiden_individual_res0.1dist1.5', 'annotation_by_markers_individual_res0.1dist1.5', 'leiden_individual_res0.1dist0.3', 'annotation_by_markers_individual_res0.1dist0.3', 'leiden_individual_res0.075dist1', 'annotation_by_markers_individual_res0.075dist1', 'leiden_individual_res0.075dist0.75', 'annotation_by_markers_individual_res0.075dist0.75', 'leiden_individual_res0.075dist0.5', 'annotation_by_markers_individual_res0.075dist0.5', 'leiden_individual_res0.075dist1.5', 'annotation_by_markers_individual_res0.075dist1.5', 'leiden_individual_res0.075dist0.3', 'annotation_by_markers_individual_res0.075dist0.3', 'leiden_individual_res0.15dist1', 'annotation_by_markers_individual_res0.15dist1', 'leiden_individual_res0.15dist0.75', 'annotation_by_markers_individual_res0.15dist0.75', 'leiden_individual_res0.15dist0.5', 'annotation_by_markers_individual_res0.15dist0.5', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1', 'leiden_individual_res0.2dist0.75', 'rank_genes_groups_leiden_individual_res0.2dist0.75', 'leiden_individual_res0.2dist0.5', 'rank_genes_groups_leiden_individual_res0.2dist0.5', 'leiden_individual_res0.2dist1.5', 'rank_genes_groups_leiden_individual_res0.2dist1.5', 'leiden_individual_res0.2dist0.3', 'rank_genes_groups_leiden_individual_res0.2dist0.3', 'leiden_individual_res0.5dist1', 'rank_genes_groups_leiden_individual_res0.5dist1', 'leiden_individual_res0.5dist0.75', 'rank_genes_groups_leiden_individual_res0.5dist0.75', 'leiden_individual_res0.5dist0.5', 'rank_genes_groups_leiden_individual_res0.5dist0.5', 'leiden_individual_res0.5dist1.5', 'rank_genes_groups_leiden_individual_res0.5dist1.5', 'leiden_individual_res0.5dist0.3', 'rank_genes_groups_leiden_individual_res0.5dist0.3', 'leiden_individual_res0.1dist1', 'rank_genes_groups_leiden_individual_res0.1dist1', 'leiden_individual_res0.1dist0.75', 'rank_genes_groups_leiden_individual_res0.1dist0.75', 'leiden_individual_res0.1dist0.5', 'rank_genes_groups_leiden_individual_res0.1dist0.5', 'leiden_individual_res0.1dist1.5', 'rank_genes_groups_leiden_individual_res0.1dist1.5', 'leiden_individual_res0.1dist0.3', 'rank_genes_groups_leiden_individual_res0.1dist0.3', 'leiden_individual_res0.075dist1', 'rank_genes_groups_leiden_individual_res0.075dist1', 'leiden_individual_res0.075dist0.75', 'rank_genes_groups_leiden_individual_res0.075dist0.75', 'leiden_individual_res0.075dist0.5', 'rank_genes_groups_leiden_individual_res0.075dist0.5', 'leiden_individual_res0.075dist1.5', 'rank_genes_groups_leiden_individual_res0.075dist1.5', 'leiden_individual_res0.075dist0.3', 'rank_genes_groups_leiden_individual_res0.075dist0.3', 'leiden_individual_res0.15dist1', 'rank_genes_groups_leiden_individual_res0.15dist1', 'leiden_individual_res0.15dist0.75', 'rank_genes_groups_leiden_individual_res0.15dist0.75', 'leiden_individual_res0.15dist0.5', 'rank_genes_groups_leiden_individual_res0.15dist0.5'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO13
================================================================================

AnnData object with n_obs × n_vars = 3595 × 18614
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY8
================================================================================

AnnData object with n_obs × n_vars = 5095 × 20381
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY7
================================================================================

AnnData object with n_obs × n_vars = 3669 × 17139
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO19
================================================================================

AnnData object with n_obs × n_vars = 5747 × 18682
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY7
================================================================================

AnnData object with n_obs × n_vars = 5265 × 20442
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO14
================================================================================

AnnData object with n_obs × n_vars = 3864 × 20113
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY1
================================================================================

AnnData object with n_obs × n_vars = 3672 × 20657
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY2
================================================================================

AnnData object with n_obs × n_vars = 3730 × 20102
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO17
================================================================================

AnnData object with n_obs × n_vars = 1321 × 12302
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_OLD_GO16
================================================================================

AnnData object with n_obs × n_vars = 18386 × 19865
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual_res0.2dist0.75', 'annotation_by_markers_individual_res0.2dist0.75', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1', 'leiden_individual_res0.2dist0.75', 'rank_genes_groups_leiden_individual_res0.2dist0.75'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_OLD_FO16
================================================================================

AnnData object with n_obs × n_vars = 4050 × 20391
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_GC_ISS-T_YNG_GY2
================================================================================

AnnData object with n_obs × n_vars = 9651 × 22116
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'



================================================================================
RRRM2_BRN_FLT_ISS-T_YNG_FY5
================================================================================

AnnData object with n_obs × n_vars = 3914 × 20509
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual'
    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts', 'n_counts', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'scrublet', 'log1p', 'hvg', 'pca', 'neighbors', 'umap', 'leiden_individual_res0.2dist1', 'rank_genes_groups_leiden_individual_res0.2dist1'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
    layers: 'counts', 'log1p', 'scaled'
    obsp: 'distances', 'connectivities'

AAACAGCCAATCGCAC-1    {'resolution': 0.1, 'min_dist': 0.3}
AAACAGCCAGCACCAT-1    {'resolution': 0.1, 'min_dist': 0.3}
AAACAGCCAGGTTCAC-1    {'resolution': 0.1, 'min_dist': 0.3}
AAACAGCCATCAATCG-1    {'resolution': 0.1, 'min_dist': 0.3}
AAACAGCCATGTCAAT-1    {'resolution': 0.1, 'min_dist': 0.3}
                                      ...                 
TTTGTTGGTCGTTATC-1    {'resolution': 0.1, 'min_dist': 0.3}
TTTGTTGGTGCAATAT-1    {'resolution': 0.1, 'min_dist': 0.3}
TTTGTTGGTGCAATGC-1    {'resolution': 0.1, 'min_dist': 0.3}
TTTGTTGGTGGTTAGC-1    {'resolution': 0.1, 'min_dist': 0.3}
TTTGTTGGTTGCACAA-1    {'resolution': 0.1, 'min_dist': 0.3}
Name: kws_cluster_individual, Length: 7547, dtype: category
Categories (1, object): ['{'resolution': 0.1, 'min_dist': 0.3}']
AAACAGCCACATACTG-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGCTACGT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGGTTCAC-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATAATCGT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATAGACTT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTTGGTAACGAGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTAGGATTT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGCTGGTG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGCTTACT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTCGGGAT-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 5465, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCACCATATG-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCACTAGCGT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGTAATAG-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATCGCTTT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATTGTTGG-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGGCAGAGGGAG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGGCATAATGTC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGGCATTAAAGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTCAGTAAT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGAACAAA-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 5842, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCACAAAGGT-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAAACTGTT-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAAAGGCCA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATATGGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATCCCTT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTTGGTCAGGCCA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTCATCCTG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGCAACTA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTACTTGC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTGTGATG-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 4922, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCACATAACT-1    {'resolution': 0.15, 'min_dist': 0.5}
AAACAGCCACCGGCTA-1    {'resolution': 0.15, 'min_dist': 0.5}
AAACAGCCACGTAATT-1    {'resolution': 0.15, 'min_dist': 0.5}
AAACAGCCAGGAACAT-1    {'resolution': 0.15, 'min_dist': 0.5}
AAACATGCAAAGGCCA-1    {'resolution': 0.15, 'min_dist': 0.5}
                                      ...                  
TTTGTGTTCTTTAGGA-1    {'resolution': 0.15, 'min_dist': 0.5}
TTTGTTGGTAACAGGG-1    {'resolution': 0.15, 'min_dist': 0.5}
TTTGTTGGTATTACCC-1    {'resolution': 0.15, 'min_dist': 0.5}
TTTGTTGGTCACCAAA-1    {'resolution': 0.15, 'min_dist': 0.5}
TTTGTTGGTTGCACGG-1    {'resolution': 0.15, 'min_dist': 0.5}
Name: kws_cluster_individual, Length: 7539, dtype: category
Categories (1, object): ['{'resolution': 0.15, 'min_dist': 0.5}']
AAACAGCCACAACAGG-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCACATTAAC-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGTCTAAT-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAGCACGTT-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAGCTTAAT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGGCAATAAGCA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGGCAGCACGTT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCACGCGGT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCCTAACGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCTAATTGG-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 3595, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAACCTTG-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATTGAAG-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCACCTGCTC-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAGGACACA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATTATGGT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCGAAGTGA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTCATTGAG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGGTTCTT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGTTTGCT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTGCTGGG-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 5095, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAATACCT-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCACCCACAG-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACAGCTTAGC-1    {'resolution': 0.2, 'min_dist': 1}
AAACCGAAGTGTTGCG-1    {'resolution': 0.2, 'min_dist': 1}
AAACCGCGTACTGAAT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCATAATCG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCATGACCG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTATTGTGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTCTAACAG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGGACCTG-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 3669, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAGTCGCT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAATGAAGC-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGGCCAAA-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATTAGCGC-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAACCCTCC-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCGATTATG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGCAAACT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCTTAAGTG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTCCCGGAA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTTATGGG-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 5747, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAATCCCTT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCACGGTACT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGTCTATG-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATAATCAC-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATAGTCAT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCCATCAGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCCGCCTAT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGCTAAAC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCTAAGGTC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTACTTGC-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 5265, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAAGCGCA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATTAGGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAGGGAGGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATGTTGCA-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACACAACCTA-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGGCATGTCAGC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGGCATTATGAC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGCACACA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGTTAGCG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTGAGGTC-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 3864, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAATACCT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGAACCGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATCCTGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAGGTTACC-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATGCTTAG-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCCCATAGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGCTAAAC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGTTATAG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGAAACAA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTACGCAA-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 3672, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAATTTGGT-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCAGTTTACG-1    {'resolution': 0.2, 'min_dist': 1}
AAACAGCCATCCCTCA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCACCTGTAA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAGCAAGAT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGGCAATGAAGC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGGCACTAGCGT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCATGCTCC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCCAAATCA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTCGCAAAC-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 3730, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACATGCAAGCCAGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATGCCTA-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACACCGGTAT-1    {'resolution': 0.2, 'min_dist': 1}
AAACCGGCAAGATTCT-1    {'resolution': 0.2, 'min_dist': 1}
AAACCGGCAGGATAAC-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCGCAACAT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCGGTTAGT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTAGGTGTC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGCTCCGT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTTGCTGT-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 1321, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAAGCGGC-1    {'resolution': 0.2, 'min_dist': 0.75}
AAACAGCCAACAGCCT-1    {'resolution': 0.2, 'min_dist': 0.75}
AAACAGCCAAGTAAGC-1    {'resolution': 0.2, 'min_dist': 0.75}
AAACAGCCAATAACGA-1    {'resolution': 0.2, 'min_dist': 0.75}
AAACAGCCACATACTG-1    {'resolution': 0.2, 'min_dist': 0.75}
                                      ...                  
TTTGTTGGTGGTGAGA-1    {'resolution': 0.2, 'min_dist': 0.75}
TTTGTTGGTTACATCC-1    {'resolution': 0.2, 'min_dist': 0.75}
TTTGTTGGTTGCAGTA-1    {'resolution': 0.2, 'min_dist': 0.75}
TTTGTTGGTTGGCGTG-1    {'resolution': 0.2, 'min_dist': 0.75}
TTTGTTGGTTTACCGT-1    {'resolution': 0.2, 'min_dist': 0.75}
Name: kws_cluster_individual, Length: 18386, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 0.75}']
AAACAGCCACCCACAG-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAATAGCAA-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACAGGAAGCC-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACAGGTCCTG-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACATGTTGTG-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCATGGTTA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCTACCTGC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTATTACCC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGCTGGTG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGGAAACG-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 4050, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCACAAAGCG-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCAACCTGGT-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCACCAACCG-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATAATGTC-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATCCCGCT-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTTGGTTAGGCGT-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTAGTTGG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTATTGCC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTCACCCA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTTACCGT-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 9651, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
AAACAGCCAAGGTGGC-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATAGCGGA-1    {'resolution': 0.2, 'min_dist': 1}
AAACATGCATCATGTG-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACACAAGCCT-1    {'resolution': 0.2, 'min_dist': 1}
AAACCAACAGGAAGCC-1    {'resolution': 0.2, 'min_dist': 1}
                                     ...                
TTTGTGTTCCCTGATC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCCGTAAAC-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTGTTCTGTGCAG-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTGTTAGCA-1    {'resolution': 0.2, 'min_dist': 1}
TTTGTTGGTTAAGTGT-1    {'resolution': 0.2, 'min_dist': 1}
Name: kws_cluster_individual, Length: 3914, dtype: category
Categories (1, object): ['{'resolution': 0.2, 'min_dist': 1}']
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Concatenate & Integrate¶

If you are concerned about hard drive space, you may want to delete file_concat and files_individual after running this cell.

In [10]:
%%time

# Integration Options
cct_available = kws_cluster is not None and (
    markers_predefined is not None)  # individual annotations available?
kws_vi = {"early_stopping": True,
          "batch_size": 1024,  # raise/lower if more/less than 16 GB VRAM
          "max_epochs": 100,
          "accelerator": "gpu",
          # "categorical_covariate_keys": covariates_categorical,
          "n_latent": 40, "n_hidden": 400}  # scVI/scANVI arguments
kws_integrate = {
    "col_celltype": cci_scanvi if cct_available else None,
    "flavor": "scanvi",
    # "flavor": "scvi",
    # "flavor": "scanorama",
    # "flavor": "harmony",
    "kws_pp": None, "kws_cluster": None,
    # "n_top_genes": 5000,
    "n_top_genes": None,
    "join": join_method, "merge": "same",
    "col_batch": None,  # suppress using batch as covariate
    "drop_non_hvgs": False,
    "use_rapids": True,
    "fill_value": np.nan if join_method == "outer" else None,
    "out_file": file_concat, **kws_vi
}

# If scVI/scANVI Integration & Individual Annotations Available
if kws_integrate["flavor"] in ["scvi", "scanvi"] and cct_available is True:
    kws_integrate.update({"unlabeled_category": unlabeled_cat})

# Integrate & Store Integration Parameters in Object
self = scflow.Rna(files_individual, col_sample=col_sample,
                  col_batch=col_batch, kws_integrate=kws_integrate)
self.rna.obs = self.rna.obs.assign(kws_integrate=str(kws_integrate))

# Write Files for Processed/Integrated Objects?
if overwrite is True or not os.path.exists(file_new):
    self.rna.write_h5ad(file_new)

# Display
print(self.rna)
self.rna.obs

>>>Concatenating data...

>>>Finding HVGs for overall data...

GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

>>>Integrating with respect to sample (SCANVI)...
	***Using counts layer for scanvi...

>>>Integrating with respect to sample (SCANVI)...
	***Setting up scVI model: {'n_latent': 40, 'n_hidden': 400}...
	***Traning scVI: {'max_epochs': 100, 'accelerator': 'gpu', 'batch_size': 1024, 'early_stopping': True}...

Training:   0%|          | 0/100 [00:00<?, ?it/s]
`Trainer.fit` stopped: `max_epochs=100` reached.

	***Setting up scANVI model: {'n_latent': 40, 'n_hidden': 400}...
	***Traning scANVI: {'max_epochs': 100, 'accelerator': 'gpu', 'batch_size': 1024, 'early_stopping': True}...
INFO     Training for 100 epochs.                                                                                  

GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

Training:   0%|          | 0/100 [00:00<?, ?it/s]
`Trainer.fit` stopped: `max_epochs=100` reached.
No description has been provided for this image 
count     12209.000000
mean      18390.727742
std       15953.886715
min         553.000000
25%        6592.000000
50%       12862.000000
75%       25092.000000
max      103242.000000
Name: n_cells_by_counts, dtype: float64
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AnnData object with n_obs × n_vars = 103274 × 12209
    obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'kws_pp_sample', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'leiden_individual_res0.2dist1', 'annotation_by_markers_individual_res0.2dist1', 'annotation_by_markers_individual_heterogeneous_collapsed', 'leiden_individual', 'annotation_by_markers_individual', 'kws_cluster_individual', '_scvi_batch', '_scvi_labels', 'annotation_scanvi', 'kws_integrate'
    var: 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
    uns: '_scvi_uuid', '_scvi_manager_uuid'
    obsm: 'X_pca', 'X_umap', 'X_scANVI', 'X_scVI', 'X_pca_old'
    layers: 'counts', 'log1p', 'scaled'
CPU times: user 13min 23s, sys: 1min 58s, total: 15min 21s
Wall time: 13min 46s
Out[10]:
Group sample Characteristics[Organism] Term Source REF Term Accession Number Characteristics[Strain] Term Source REF.1 Term Accession Number.1 Characteristics[Genotype] Term Source REF.2 Term Accession Number.2 Characteristics[Animal Source] Characteristics[Sex] Term Source REF.3 Term Accession Number.3 Factor Value[Spaceflight] Term Source REF.4 Term Accession Number.4 Factor Value[Age] Unit Term Source REF.5 Term Accession Number.5 Characteristics[Material Type] Term Source REF.6 Term Accession Number.6 Characteristics[diet] Characteristics[Feeding Schedule] Characteristics[Age at Euthanasia] Unit.1 Term Source REF.7 Term Accession Number.7 Protocol REF Parameter Value[habitat] Parameter Value[Enrichment material] Parameter Value[duration] Unit.2 Term Source REF.8 Term Accession Number.8 Parameter Value[light cycle] Protocol REF.1 Parameter Value[Euthanasia Method] Parameter Value[Sample Preservation Method] Term Source REF.9 Term Accession Number.9 Parameter Value[Sample Storage Temperature] Unit.3 Term Source REF.10 Term Accession Number.10 Comment[RFID] Comment[Euthanasia Date] n_cells_original_sample kws_pp_sample n_genes_by_counts total_counts log1p_n_genes_by_counts log1p_total_counts total_counts_mt pct_counts_mt log1p_total_counts_mt total_counts_ribo pct_counts_ribo log1p_total_counts_ribo total_counts_hb pct_counts_hb log1p_total_counts_hb n_counts n_genes doublet_score predicted_doublet leiden_individual_res0.2dist1 annotation_by_markers_individual_res0.2dist1 annotation_by_markers_individual_heterogeneous_collapsed leiden_individual annotation_by_markers_individual kws_cluster_individual _scvi_batch _scvi_labels annotation_scanvi kws_integrate
AAACAGCCAATCGCAC-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 Ground Control | 20 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY4 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Ground Control OSD https://osdr.nasa.gov/ 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Liquid Nitrogen NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E2A671967 18-Sep-2019 8542 {'min_max_genes': [321.0, None], 'min_max_cell... 2074 11854.281250 7.637716 9.380528 35.685387 0.301034 3.602379 48.519485 0.409299 3.902366 6.004995 0.050657 1.946623 4962.0 2152 0.030852 False 3 Neuron Neuron 3 Neuron {'resolution': 0.1, 'min_dist': 0.3} 0 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
AAACAGCCAGCACCAT-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 Ground Control | 20 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY4 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Ground Control OSD https://osdr.nasa.gov/ 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Liquid Nitrogen NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E2A671967 18-Sep-2019 8542 {'min_max_genes': [321.0, None], 'min_max_cell... 1568 9592.913086 7.358194 9.168884 36.192043 0.377279 3.616095 53.094116 0.553472 3.990726 5.822495 0.060696 1.920225 2994.0 1636 0.052691 False 13 Neuron Neuron 12 Neuron {'resolution': 0.1, 'min_dist': 0.3} 0 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
AAACAGCCAGGTTCAC-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 Ground Control | 20 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY4 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Ground Control OSD https://osdr.nasa.gov/ 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Liquid Nitrogen NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E2A671967 18-Sep-2019 8542 {'min_max_genes': [321.0, None], 'min_max_cell... 2953 15581.877930 7.990915 9.653928 9.529344 0.061157 2.354166 45.639675 0.292902 3.842452 0.000000 0.000000 0.000000 8625.0 3080 0.014899 False 1 Neuron Neuron 2 Neuron {'resolution': 0.1, 'min_dist': 0.3} 0 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
AAACAGCCATCAATCG-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 Ground Control | 20 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY4 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Ground Control OSD https://osdr.nasa.gov/ 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Liquid Nitrogen NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E2A671967 18-Sep-2019 8542 {'min_max_genes': [321.0, None], 'min_max_cell... 1071 6977.024902 6.977281 8.850521 14.213669 0.203721 2.722194 31.060627 0.445184 3.467629 0.000000 0.000000 0.000000 2017.0 1116 0.018405 False 8 Oligodendrocyte Oligodendrocyte 6 Oligodendrocyte {'resolution': 0.1, 'min_dist': 0.3} 0 0 Oligodendrocyte {'col_celltype': 'annotation_by_markers_indivi...
AAACAGCCATGTCAAT-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 Ground Control | 20 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY4 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Ground Control OSD https://osdr.nasa.gov/ 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Liquid Nitrogen NCIT http://purl.obolibrary.org/obo/NCIT_C68796 -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E2A671967 18-Sep-2019 8542 {'min_max_genes': [321.0, None], 'min_max_cell... 2527 13878.588867 7.835184 9.538175 24.442604 0.176117 3.236425 46.229156 0.333097 3.855011 0.000000 0.000000 0.000000 6688.0 2614 0.004741 False 1 Neuron Neuron 2 Neuron {'resolution': 0.1, 'min_dist': 0.3} 0 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
TTTGTGTTCCCTGATC-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 Space Flight | 20 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Space Flight MESH http://purl.bioontology.org/ontology/MESH/D013026 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Cryochiller OSD https://osdr.nasa.gov/ -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E3E102A12 16-Sep-2019 4714 {'min_max_genes': [809.825, None], 'min_max_ce... 3951 19522.046875 8.281977 9.879351 48.609306 0.248997 3.904178 191.109528 0.978942 5.258066 0.000000 0.000000 0.000000 12819.0 4151 0.006702 False 6 Neuron Neuron 6 Neuron {'resolution': 0.2, 'min_dist': 1} 17 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
TTTGTGTTCCGTAAAC-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 Space Flight | 20 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Space Flight MESH http://purl.bioontology.org/ontology/MESH/D013026 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Cryochiller OSD https://osdr.nasa.gov/ -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E3E102A12 16-Sep-2019 4714 {'min_max_genes': [809.825, None], 'min_max_ce... 4371 21093.955078 8.382976 9.956789 31.026888 0.147089 3.466576 292.743744 1.387809 5.682708 4.184459 0.019837 1.645665 15502.0 4645 0.007560 False 3 Neuron Neuron 3 Neuron {'resolution': 0.2, 'min_dist': 1} 17 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
TTTGTGTTCTGTGCAG-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 Space Flight | 20 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Space Flight MESH http://purl.bioontology.org/ontology/MESH/D013026 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Cryochiller OSD https://osdr.nasa.gov/ -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E3E102A12 16-Sep-2019 4714 {'min_max_genes': [809.825, None], 'min_max_ce... 5541 24471.394531 8.620111 10.105301 58.827507 0.240393 4.091465 238.306396 0.973816 5.477745 0.000000 0.000000 0.000000 27623.0 5886 0.035294 False 4 Neuron Neuron 4 Neuron {'resolution': 0.2, 'min_dist': 1} 17 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...
TTTGTTGGTGTTAGCA-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 Space Flight | 20 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Space Flight MESH http://purl.bioontology.org/ontology/MESH/D013026 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Cryochiller OSD https://osdr.nasa.gov/ -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E3E102A12 16-Sep-2019 4714 {'min_max_genes': [809.825, None], 'min_max_ce... 845 5715.998535 6.740519 8.651199 60.961349 1.066504 4.126511 86.941833 1.521026 4.476676 0.000000 0.000000 0.000000 1469.0 873 0.007560 False 13 Oligodendrocyte Oligodendrocyte 13 Oligodendrocyte {'resolution': 0.2, 'min_dist': 1} 17 0 Oligodendrocyte {'col_celltype': 'annotation_by_markers_indivi...
TTTGTTGGTTAAGTGT-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 Space Flight | 20 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY5 Mus musculus NCBITAXON http://purl.bioontology.org/ontology/NCBITAXON... C57BL/6NTac EFO http://www.ebi.ac.uk/efo/EFO_0020093 Wild Type NCIT http://purl.obolibrary.org/obo/NCIT_C62195 Taconic Biosciences Female MESH http://purl.bioontology.org/ontology/MESH/D005260 Space Flight MESH http://purl.bioontology.org/ontology/MESH/D013026 12 week UO http://purl.obolibrary.org/obo/UO_0000034 Left cerebral hemisphere FMA http://purl.org/sig/ont/fma/fma61819 Nutrient Upgraded Rodent Food Bar (NuRFB) ad libitum 20 week UO http://purl.obolibrary.org/obo/UO_0000034 Animal Husbandry Rodent Flight Hardware (Transporter and Habitat) Hut 53 day UO http://purl.obolibrary.org/obo/UO_0000033 12 h light/dark cycle sample collection Bilateral thoracotomy with sedation, Cardiac p... Cryochiller OSD https://osdr.nasa.gov/ -80 degree Celsius UO http://purl.obolibrary.org/obo/UO_0000027 6E3E102A12 16-Sep-2019 4714 {'min_max_genes': [809.825, None], 'min_max_ce... 3268 17057.070312 8.092239 9.744379 24.902000 0.145992 3.254320 154.146927 0.903713 5.044373 5.381324 0.031549 1.853376 9258.0 3411 0.058924 False 4 Neuron Neuron 4 Neuron {'resolution': 0.2, 'min_dist': 1} 17 0 Neuron {'col_celltype': 'annotation_by_markers_indivi...

103274 rows × 79 columns

Benchmark Integration¶

In [11]:
# if kws_integrate["flavor"] == "scanvi":
#     try:
#         results_integration_benchmark = scflow.pp.benchmark_integration(
#             self.rna, col_sample, col_celltype="annotation_scanvi")
#     except Exception as err:
#         print(err)

Clustering¶

Perform PCA, UMAP embedding, and Leiden clustering on the integrated object

In [21]:
%%time


# Clustering Options
resolution, min_dist = 0.025, 1
# resolution, min_dist = 0.027, 1.3
# resolution, min_dist = 0.012, 1.5
# resolution, min_dist = 0.027, 1.3
# resolution, min_dist = 0.035, 1.3
# resolution, min_dist = 0.02, 0.6
# resolution, min_dist = 0.018, 0.7
# resolution, min_dist = 0.012, 0.5
# resolution, min_dist = 0.0115, 1.5
# resolution, min_dist = 0.011, 0.5
n_neighbors = 40
cct = "leiden"  # key added/column name

# Clustering
self.cluster(col_celltype=cct, resolution=resolution, min_dist=min_dist,
             kws_pca=False, layer="scaled",
             kws_neighbors=dict(n_neighbors=n_neighbors))  # cluster
self.rna.obs = self.rna.obs.assign(**{
       f"{cct}_resolution": resolution}).assign(
              **{f"{cct}_n_neighbors": n_neighbors}).assign(
                     **{f"{cct}_min_dist": min_dist})  # store parameters
_ = self.plot(kind="umap", wspace=0.5, palette="tab20",
              color=[col_batch, "leiden", col_sample])  # plot UMAP
print(self.rna.obs["leiden"].value_counts().to_frame("n_cells"))  # N/cluster
print(self.rna.obs.groupby(col_sample).apply(lambda x: x[
       cct].value_counts(), include_groups=False).unstack(1))  # by group
self.find_markers(col_celltype=cct)  # DEGs by cluster
col_celltype = "leiden"
self._info["col_celltype"] = col_celltype

	***Building neighborhood with 40 neighbors...
	***Embedding UMAP with minimum distance 1...
	***Performing Leiden clustering with resolution 0.025...
        n_cells
leiden         
0         21527
1         19639
5         19269
2         18451
6          8753
4          7160
8          3200
7          3117
3          2158
leiden                            0     1     2     3    4     5     6    7    8
sample                                                                          
RRRM2_BRN_GC_ISS-T_YNG_GY4      610  1491  1219  1237  552  1071   946  190  231
RRRM2_BRN_GC_ISS-T_YNG_GY9      420  1394   908    69  493  1066   731  197  187
RRRM2_BRN_GC_ISS-T_OLD_GO18     490  1114   801    44  559  1432  1039  177  186
RRRM2_BRN_FLT_ISS-T_OLD_FO20    445   833  1242    60  347  1141   500  173  181
RRRM2_BRN_GC_ISS-T_OLD_GO19     591  1619  1365    68  522  1404  1447  255  268
RRRM2_BRN_GC_ISS-T_OLD_GO13     213   960   989    74  341   531   267  132   88
RRRM2_BRN_FLT_ISS-T_YNG_FY8     236  1686  1194    32  422   532   485  219  289
RRRM2_BRN_FLT_ISS-T_YNG_FY7     244   936  1047    38  257   755   102  127  163
RRRM2_BRN_FLT_ISS-T_OLD_FO19   1851   734   762    62  323   703  1047  139  126
RRRM2_BRN_GC_ISS-T_YNG_GY7      312  1491  1205    88  508   851   428  209  173
RRRM2_BRN_FLT_ISS-T_OLD_FO14    352   687   899    31  308  1190   105  149  143
RRRM2_BRN_GC_ISS-T_YNG_GY1      309   893   767    35  319   975   124  131  119
RRRM2_BRN_FLT_ISS-T_YNG_FY2     388   765   896    20  296  1015    26  152  172
RRRM2_BRN_FLT_ISS-T_OLD_FO17    722    94   135    20   39   137   126   21   27
RRRM2_BRN_GC_ISS-T_OLD_GO16   12847  1026  1223   130  536  1564   637  217  206
RRRM2_BRN_FLT_ISS-T_OLD_FO16    301   878   768    20  299  1359   117  130  178
RRRM2_BRN_GC_ISS-T_YNG_GY2      815  2444  2346   109  747  2205   319  354  312
RRRM2_BRN_FLT_ISS-T_YNG_FY5     381   594   685    21  292  1338   307  145  151
No description has been provided for this image
No description has been provided for this image 
CPU times: user 25.5 s, sys: 2.28 s, total: 27.8 s
Wall time: 10.5 s

Sub-Clustering¶

Sub-cluster the biggest <subcluster_biggest> Leiden clusters?

For instance if subcluster_biggest = 3, take top three most abundant (highest cell count) clusters and sub-cluster them.

In [22]:
resolution_sub = None if subcluster_biggest is False else 0.005
if subcluster_biggest is not False:
    col_celltype = "leiden_subcluster"
    biggest_clusters = self.rna.obs["leiden"].value_counts().index.values[
        :min(len(self.rna.obs["leiden"].unique()), subcluster_biggest)]
    self.rna.obs.loc[:, col_celltype] = self.rna.obs.leiden.copy()
    for x in biggest_clusters:
        sc.tl.leiden(self.rna, restrict_to=(col_celltype, [x]),
                     key_added=col_celltype, resolution=resolution_sub,
                     seed=0, n_iterations=-1, use_weights=True)
    self.find_markers(col_celltype=col_celltype)  # DEGs by cluster
    self.plot(kind="umap", color=col_celltype)
    print(self.rna.obs[[col_sample, col_celltype]].groupby(
        col_sample).value_counts().unstack(1))
    self.rna.obs.loc[:, f"resolution_{col_celltype}"] = resolution_sub
    self._info["col_celltype"] = col_celltype

Annotate¶

Annotate cell types with various methods

Examine Marker Genes¶

This code looks at top markers by log2fold-change and adjusted p-value cutoffs and sorts by adjusted p-values.

Use kind = ["heat", "dot"] to get dot plots too.

In [23]:
markers_df = self.get_markers_df(
    n_genes=15, col_celltype=col_celltype,
    p_threshold=1e-10, log2fc_threshold=1.5, log2fc_threshold_abs=False)
markers_dict = dict(markers_df.groupby(col_celltype).apply(
    lambda x: list(x.reset_index().names)))
_ = self.plot(genes=markers_dict, figsize=(15, 15), vmin=0, vmax=0.5,
              layer="scaled", standard_scale="obs", kind="heat")
markers_df

WARNING: Gene labels are not shown when more than 50 genes are visualized. To show gene labels set `show_gene_labels=True`
No description has been provided for this image
Out[23]:
scores logfoldchanges pvals pvals_adj
leiden names
5 Smurf1 52.179630 1.787631 0.000000e+00 0.000000e+00
Arl3 52.371861 1.716211 0.000000e+00 0.000000e+00
Pfkm 52.359268 1.858708 0.000000e+00 0.000000e+00
Usf3 52.342121 1.884558 0.000000e+00 0.000000e+00
Ccn3 52.329323 3.249097 0.000000e+00 0.000000e+00
Gm12371 52.299938 3.280986 0.000000e+00 0.000000e+00
Ubac2 52.276756 1.724732 0.000000e+00 0.000000e+00
Entr1 52.269951 1.826661 0.000000e+00 0.000000e+00
Tmem151a 52.265415 1.772315 0.000000e+00 0.000000e+00
Nceh1 52.231071 1.787486 0.000000e+00 0.000000e+00
Pls3 52.228706 1.889730 0.000000e+00 0.000000e+00
Mrpl41 52.227352 1.842142 0.000000e+00 0.000000e+00
Ncs1 52.216713 1.837653 0.000000e+00 0.000000e+00
Adk 52.202930 1.626228 0.000000e+00 0.000000e+00
Btbd7 52.185795 1.655978 0.000000e+00 0.000000e+00
1 Ipo9 49.887600 1.806189 0.000000e+00 0.000000e+00
Kif16b 49.997070 1.691769 0.000000e+00 0.000000e+00
Slc39a6 49.989765 2.303841 0.000000e+00 0.000000e+00
Ssb 49.983730 1.716834 0.000000e+00 0.000000e+00
Ubac2 49.975971 1.678680 0.000000e+00 0.000000e+00
9330159M07Rik 49.970547 2.704767 0.000000e+00 0.000000e+00
Erp29 49.963879 1.781210 0.000000e+00 0.000000e+00
Gpc1 49.954193 2.234042 0.000000e+00 0.000000e+00
Reps1 49.947380 1.714474 0.000000e+00 0.000000e+00
Vstm5 49.922413 2.318811 0.000000e+00 0.000000e+00
Mapkap1 49.919682 1.612838 0.000000e+00 0.000000e+00
Tmem169 49.919678 2.276560 0.000000e+00 0.000000e+00
Clpx 49.903904 1.756471 0.000000e+00 0.000000e+00
Map3k7 49.903728 1.696558 0.000000e+00 0.000000e+00
Btaf1 49.999718 1.586228 0.000000e+00 0.000000e+00
0 Nrg3 142.452026 4.107781 0.000000e+00 0.000000e+00
Dpp10 138.951385 4.910851 0.000000e+00 0.000000e+00
Kcnip4 138.293945 4.104700 0.000000e+00 0.000000e+00
Rbfox1 137.393112 3.926455 0.000000e+00 0.000000e+00
Celf2 131.670334 3.144436 0.000000e+00 0.000000e+00
Csmd1 128.779266 3.637968 0.000000e+00 0.000000e+00
Nrxn3 125.969872 3.488383 0.000000e+00 0.000000e+00
Nrg1 113.331749 3.747945 0.000000e+00 0.000000e+00
Syt1 112.355385 3.356306 0.000000e+00 0.000000e+00
Meg3 111.013176 3.171908 0.000000e+00 0.000000e+00
Sgcz 100.021637 3.458776 0.000000e+00 0.000000e+00
Fgf14 99.979164 2.813265 0.000000e+00 0.000000e+00
Ptprd 97.597748 1.844778 0.000000e+00 0.000000e+00
Cntnap2 93.390694 2.978568 0.000000e+00 0.000000e+00
Snhg11 90.035690 2.944553 0.000000e+00 0.000000e+00
2 Kcnk13 59.093266 3.053705 0.000000e+00 0.000000e+00
Ywhaq 56.939629 2.365501 0.000000e+00 0.000000e+00
App 57.157963 1.881855 0.000000e+00 0.000000e+00
Mboat1 57.354801 4.658205 0.000000e+00 0.000000e+00
Josd2 57.376133 2.848171 0.000000e+00 0.000000e+00
Kif6 57.394062 3.345257 0.000000e+00 0.000000e+00
Sh3tc2 57.528534 6.165985 0.000000e+00 0.000000e+00
Cipc 57.682247 2.468949 0.000000e+00 0.000000e+00
Tcf12 57.921982 2.065799 0.000000e+00 0.000000e+00
Shtn1 58.287922 2.404106 0.000000e+00 0.000000e+00
Kctd3 58.620754 2.627975 0.000000e+00 0.000000e+00
Tsc22d3 59.000103 3.219590 0.000000e+00 0.000000e+00
Rhog 56.667194 4.276067 0.000000e+00 0.000000e+00
Lgi3 59.205410 2.762129 0.000000e+00 0.000000e+00
Selenop 59.312222 3.112374 0.000000e+00 0.000000e+00
8 Tgfbr1 137.340317 10.769904 0.000000e+00 0.000000e+00
Hexb 119.997032 8.486044 0.000000e+00 0.000000e+00
Mertk 116.801888 9.555871 0.000000e+00 0.000000e+00
Cst3 112.845932 7.582945 0.000000e+00 0.000000e+00
Inpp5d 111.094070 12.416537 0.000000e+00 0.000000e+00
Srgap2 110.366783 6.584876 0.000000e+00 0.000000e+00
Dock8 82.764763 11.058674 0.000000e+00 0.000000e+00
Ly86 77.547310 10.962577 0.000000e+00 0.000000e+00
Mef2a 77.216774 5.439650 0.000000e+00 0.000000e+00
Dock2 75.843651 10.114684 0.000000e+00 0.000000e+00
Fyb 75.524727 10.855298 0.000000e+00 0.000000e+00
Qk 63.830379 3.882025 0.000000e+00 0.000000e+00
Gpr34 63.233471 9.749660 0.000000e+00 0.000000e+00
Lrmda 63.180691 6.527174 0.000000e+00 0.000000e+00
Ccnd3 62.310600 7.042611 0.000000e+00 0.000000e+00
7 Ptprz1 280.503510 9.448026 0.000000e+00 0.000000e+00
Lhfpl3 263.645355 8.650301 0.000000e+00 0.000000e+00
Sox6 257.039917 10.220982 0.000000e+00 0.000000e+00
Grid2 142.837418 3.889154 0.000000e+00 0.000000e+00
Npas3 138.383698 6.207528 0.000000e+00 0.000000e+00
Epn2 130.680801 6.545508 0.000000e+00 0.000000e+00
Sox2ot 130.439926 7.050704 0.000000e+00 0.000000e+00
Sgcd 129.128464 6.576615 0.000000e+00 0.000000e+00
Slc35f1 125.385567 5.852885 0.000000e+00 0.000000e+00
Pdgfra 124.127045 12.550479 0.000000e+00 0.000000e+00
Qk 120.170456 4.371033 0.000000e+00 0.000000e+00
Nrxn1 112.409256 3.198757 0.000000e+00 0.000000e+00
Adgrl3 89.407158 2.946114 0.000000e+00 0.000000e+00
Megf11 88.544106 7.980099 0.000000e+00 0.000000e+00
Ntm 87.639534 3.522771 0.000000e+00 0.000000e+00
6 Agap2 50.225891 2.618914 0.000000e+00 0.000000e+00
Ahcyl2 50.588196 2.080101 0.000000e+00 0.000000e+00
Sh3kbp1 50.558674 2.299337 0.000000e+00 0.000000e+00
Cd47 50.503082 2.158016 0.000000e+00 0.000000e+00
Pten 50.455803 2.041422 0.000000e+00 0.000000e+00
Ttyh1 50.349094 2.331339 0.000000e+00 0.000000e+00
Gm50024 50.348148 4.869073 0.000000e+00 0.000000e+00
Cux1 50.324490 2.190573 0.000000e+00 0.000000e+00
Far1 50.277599 2.102910 0.000000e+00 0.000000e+00
Prelid3a 50.617641 2.529395 0.000000e+00 0.000000e+00
Ncdn 51.535789 2.577785 0.000000e+00 0.000000e+00
Tpm1 51.532272 2.544296 0.000000e+00 0.000000e+00
Fkbp1a 51.465649 2.445119 0.000000e+00 0.000000e+00
Nfx1 51.442070 2.360921 0.000000e+00 0.000000e+00
C2cd2l 51.227360 2.870124 0.000000e+00 0.000000e+00
3 Zbtb20 114.934196 4.832954 0.000000e+00 0.000000e+00
Cpq 40.541790 5.582988 2.805311e-268 3.491339e-267
Utrn 36.233761 4.225976 4.815009e-226 5.067797e-225
Airn 35.226917 5.072333 1.722178e-215 1.727697e-214
Phldb2 33.469276 5.959682 2.617537e-198 2.432079e-197
Cped1 33.102448 6.261213 9.003496e-195 8.252529e-194
Pard3b 32.873741 4.236024 4.643844e-193 4.224791e-192
Sdk1 32.677834 4.139126 2.488597e-191 2.242308e-190
Kcnh8 32.520973 5.330007 2.456451e-189 2.185919e-188
Rora 31.530457 2.573417 1.057188e-181 8.957121e-181
9530026P05Rik 31.518534 4.679823 5.832826e-180 4.928234e-179
Esr1 29.709341 5.484935 5.565380e-163 4.344484e-162
Hmcn1 28.951281 6.202574 4.982675e-156 3.738997e-155
Ptprk 27.131479 2.951380 1.800068e-140 1.237446e-139
Greb1 26.736805 6.318375 2.751495e-136 1.829684e-135
4 Aldh1l1 47.545361 5.246835 0.000000e+00 0.000000e+00
Fmn2 46.399601 2.557196 0.000000e+00 0.000000e+00
Hif3a 46.974819 5.359393 0.000000e+00 0.000000e+00
Nrcam 47.036221 2.432620 0.000000e+00 0.000000e+00
Prrx1 47.117741 5.187209 0.000000e+00 0.000000e+00
Ston2 47.203751 3.795210 0.000000e+00 0.000000e+00
Psd2 47.237278 3.946349 0.000000e+00 0.000000e+00
Folh1 47.321003 3.813559 0.000000e+00 0.000000e+00
Selenop 47.366066 3.567869 0.000000e+00 0.000000e+00
Adhfe1 46.309509 5.024072 0.000000e+00 0.000000e+00
Timp3 47.647545 3.607164 0.000000e+00 0.000000e+00
Gna13 47.824993 3.409621 0.000000e+00 0.000000e+00
Vegfa 47.843952 3.688154 0.000000e+00 0.000000e+00
Msmo1 48.290367 3.307666 0.000000e+00 0.000000e+00
Son 48.297272 2.408797 0.000000e+00 0.000000e+00

Annotate by Marker Gene Overlap¶

File from https://github.com/nasa/GeneLab_Data_Processing/blob/master/scRNAseq/10X_Chromium_3prime_Data/GeneLab_CellType_GeneMarkers/GL-DPPD-7111_GeneMarker_Files/GL-DPPD-7111_Mmus_Brain_CellType_GeneMarkers.csv

Example of the Expected Marker Definition Format

markers_predefined = {
    "CD4 T cells": {"IL7R"},
    "CD14+ Monocytes": {"CD14", "LYZ"},
    "B cells": {"MS4A1"},
    "CD8 T cells": {"CD8A"},
    "NK cells": {"GNLY", "NKG7"},
    "FCGR3A+ Monocytes": {"FCGR3A", "MS4A7"},
    "Dendritic Cells": {"FCER1A", "CST3"},
    "Megakaryocytes": {"PPBP"},
}
In [30]:
# Annotate
marker_matches = self.annotate(
    mks_collapsed,
    # celltypes_superhierarchical=celltypes_superhierarchical,
    col_celltype=col_celltype, col_celltype_new="annotation_by_overlap",
    top_n_markers=50,  # can only have this one or `adj_pval_threshold`
    # adj_pval_threshold=1e-10,
    # method="overlap_coef",
    method="overlap_count",
    overwrite=True)

# Rename Any Labels As Pre-Specified
if rename_marker_based_annotation is not None:
    self.rna.obs.loc[:, "annotation_by_overlap"] = self.rna.obs[
        "annotation_by_overlap"].replace(rename_marker_based_annotation)

# Print & Plot Results
self.plot(kind="umap", color="annotation_by_overlap", wspace=0.4)
print(round(self.rna.obs["annotation_by_overlap"].value_counts(
    normalize=True) * 100, 2))
round(self.rna.obs[[col_celltype, "annotation_by_overlap"]
                   ].value_counts(normalize=True)* 100, 2).sort_values()

annotation_by_overlap
Neuron             66.99
Oligodendrocyte    17.87
Astrocyte           6.93
Microglial          3.10
OPC                 3.02
Endothelial         2.09
Name: proportion, dtype: float64
Out[30]:
leiden  annotation_by_overlap
3       Endothelial               2.09
7       OPC                       3.02
8       Microglial                3.10
4       Astrocyte                 6.93
6       Neuron                    8.48
2       Oligodendrocyte          17.87
5       Neuron                   18.66
1       Neuron                   19.02
0       Neuron                   20.84
Name: proportion, dtype: float64
No description has been provided for this image

Annotate with ToppGene¶

In [37]:
# Options
min_genes = 2  # minimum markers that have to overlap between Leiden & atlas
remove_strings = ["----L1-6", # "---[|]M.*",
                  "facs-", "-nan-",
                  # "-i_Gaba_3-.*",
                  "Brain_organoid-organoid_Kanton_Nature-Organoid-..-",
                  # "Non-neuronal-Macroglial-((^|)(Oligo|Astro))+-",
                  # "-Glut_E.*IL7R",
                  "cells hierarchy compared to all cells using T-S.*",
                  ".*-organoid_Tanaka_cellReport-.+-",
                  "...BrainAtlas -.*", "-eN2.*", "...Sample groups.*",
                  "...Sample Type, Dataset.*",
                  "-Neuronal",
                  " // Primary Cells by Cluster",
                  ".World...Primary Cells by Cluster",
                  "Brain_organoid-organoid_Velasco_nature-6_",
                  "Fetal_brain-fetalBrain_Zhong_nature-....-",
                  "Somatosensory_Cortex_....-Neuronal-",
                  "Non-neuronal-Non-dividing-",
                  "...Sample groups..6 Anatomical region groups., with 5.*",
                  "Brain_organoid-organoid_Paulsen_bioRxiv-",
                  "-Glut_E_(THEMIS)", "[(]THEMIS[)]",  # "[|].*",
                  "- method, tissue, subtissue, age, lineage.*"]
drop_name_patterns = ["striatum", "globus", "Entopeduncular",
                      "Substantia_nigra-", "Thalamus-"]
toppgene_rename_by_pattern = dict(
    Inhibitory=["Inh(_|ib)", "GABA"], Excitatory=["Excit", "Glut"],
    # Inhibitory=["Inh(_|ib)"], Excitatory=["Excit"],
    # # Gabaergic=["GABA"], Glutamatergic=["Glut"],
    Astrocyte=["Astrocyte","Astroglia", "Macroglial-Astro"],
    Microglial=["Microglia", "Micro"],
    Endothelial=["Endothelial"],
    Oligodendrocyte=[r"^(?=.*oligo)(?!.*poly)(?!.*opc).*"],
    OPC=["Polydendrocyte", "OPC"])
drop_regions = [
    "Mid-temporal_gyrus_(MTG)", "primary_auditory_cortex_(A1C)",
    "Somatosensory_Cortex_(S1)", "Anterior_Cingulate_gyrus_(CgG)",
    "Primary_Motor_Cortex_(M1)",
    "Mid-temporal_gyrus_(MTG)|Mid-temporal_gyrus_(MTG)",
    "primary_auditory_cortex_(A1C)|primary_auditory_cortex_(A1C)",
    "Somatosensory_Cortex_(S1)|Somatosensory_Cortex_(S1)",
    "Anterior_Cingulate_gyrus_(CgG)|Anterior_Cingulate_gyrus_(CgG)",
    "Primary_Motor_Cortex_(M1)|Primary_Motor_Cortex_(M1)",
    r"Neuronal|World / ",
    "Primary_Visual_cortex_(V1C)|Primary_Visual_cortex_(V1C)",
    "mon",
    "BMP_responsible_cell|6m", "bearing_cell|6m", "bearing_cell|GW16", "11",
    "Non-neuronal-Non-dividing",
    "Frontal_cortex|Frontal_cortex",
    "Primary_Visual_cortex_(V1C)", "Substantia_nigra",
    "Thalamus", "Hippocampus", "Frontal_cortex"
]  # remove if name is just a region or top-level hierarchical/undesired type
drop_regions = drop_regions + [f"{i}-Non-neuronal" for i in drop_regions]

# Query ToppGene
results_toppgene = scflow.pp.annotate_by_toppgene(
    markers_dict, remove_strings=remove_strings,
    species=species, min_genes=min_genes, source_patterns=source_patterns)

# Remove or Alter Certain Name Patterns
drop_names = results_toppgene.Name.apply(lambda x: not any((
    i.lower() in x.lower() for i in drop_name_patterns)))
results_toppgene = results_toppgene[drop_names]
rn_tg = results_toppgene.Name.apply(lambda x: {x: " | ".join([
    j for j in toppgene_rename_by_pattern if any((re.search(i.lower(
        ), x.lower()) is not None for i in toppgene_rename_by_pattern[
            j]))])}).apply(lambda x: np.nan if x[list(x.keys())[
                0]] == "" else x).dropna().reset_index(drop=True).apply(
                    lambda x: pd.Series(x)).stack().reset_index(
                        0, drop=True)  # renaming guide
results_toppgene = results_toppgene.replace({"Name": dict(rn_tg)})
results_toppgene = results_toppgene[~results_toppgene.Name.isin(drop_regions)]

# Map Labels (Plurality Vote If Sufficient or Top)
top_cs = dict(results_toppgene.groupby(
    "Gene Set").apply(lambda x: x.Name.iloc[:10].value_counts().index.values[
        0] if x.Name.iloc[:10].value_counts(
            normalize=True).iloc[0] >= 0.25 else x.Name[0]))
print("\n".join([f"{k}: {top_cs[k]}" for k in top_cs]), "\n\n")
if "annotation_toppgene" in self.rna.obs:
    self.rna.obs = self.rna.obs.drop("annotation_toppgene", axis=1)
self.rna.obs = self.rna.obs.join(self.rna.obs[col_celltype].replace(
    top_cs).to_frame("annotation_toppgene")).loc[self.rna.obs.index]

# Display Results
if "annotation_by_overlap" in self.rna.obs:
    print(round(self.rna.obs[["annotation_toppgene", "annotation_by_overlap"]
                             ].value_counts(normalize=True).sort_index(
                                 ) * 100, 2), "\n\n")
print(round(self.rna.obs["annotation_toppgene"].value_counts(
    normalize=True) * 100, 2))
results_toppgene.reset_index("ID", drop=True).drop([
    "QValueBonferroni", "QValueFDRBY", "QValueFDRBH",
    "TotalGenes", "Genes"], axis=1)

Server returned HTTP status code: 400
Content: {'error': 'No valid lookup found for symbol Qk'}
Server returned HTTP status code: 400
Content: {'error': 'No valid lookup found for symbol Fyb'}
Server returned HTTP status code: 400
Content: {'error': 'No valid lookup found for symbol Qk'}
0: OPC
2: Oligodendrocyte
3: Excitatory
4: Astrocyte
6: Excitatory
7: OPC
8: Microglial 


annotation_toppgene  annotation_by_overlap
OPC                  Neuron                   20.84
                     OPC                       3.02
1                    Neuron                   19.02
Oligodendrocyte      Oligodendrocyte          17.87
Excitatory           Neuron                    8.48
                     Endothelial               2.09
Astrocyte            Astrocyte                 6.93
5                    Neuron                   18.66
Microglial           Microglial                3.10
Name: proportion, dtype: float64 


annotation_toppgene
OPC                23.86
1                  19.02
5                  18.66
Oligodendrocyte    17.87
Excitatory         10.57
Astrocyte           6.93
Microglial          3.10
Name: proportion, dtype: float64
Out[37]:
Name PValue GenesInTerm GenesInQuery GenesInTermInQuery Source Name_Original percent_atlas_genes_in_query Symbols
Gene Set Category
0 ToppCell Excitatory 0.0 195 14 9 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... 4.615385 [CELF2, SYT1, NRG1, KCNIP4, MEG3, RBFOX1, PTPR...
ToppCell OPC 0.0 200 14 6 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA---|Macroglia... 3.0 [KCNIP4, SGCZ, FGF14, MEG3, RBFOX1, CSMD1]
ToppCell OPC 0.0 200 14 6 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA-|Macroglial ... 3.0 [KCNIP4, SGCZ, FGF14, MEG3, RBFOX1, CSMD1]
ToppCell OPC 0.0 200 14 6 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA--|Macroglial... 3.0 [KCNIP4, SGCZ, FGF14, MEG3, RBFOX1, CSMD1]
ToppCell OPC 0.0 200 14 6 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA----L1-6|Macr... 3.0 [KCNIP4, SGCZ, FGF14, MEG3, RBFOX1, CSMD1]
ToppCell OPC 0.0 200 14 6 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA|Macroglial /... 3.0 [KCNIP4, SGCZ, FGF14, MEG3, RBFOX1, CSMD1]
ToppCell OPC 0.0 200 14 6 Human Adult Brain MTG Macroglial-Polydendrocytes|Macroglial / cells ... 3.0 [KCNIP4, SGCZ, FGF14, MEG3, RBFOX1, CSMD1]
ToppCell Substantia_nigra|Substantia_nigra 0.0 186 14 5 Mouse Adult Brain Overview (690k cells, 9 regi... Substantia_nigra-Neuronal|Substantia_nigra / B... 2.688172 [SYT1, KCNIP4, MEG3, NRXN3, CNTNAP2]
ToppCell Excitatory 0.0 192 14 5 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Neuronal-Glutamaterg... 2.604167 [CELF2, NRG1, KCNIP4, DPP10, PTPRD]
ToppCell Inhibitory 0.0 194 14 5 Comparison of human cortex and organoids Neuron-Postmitotic-Inhibitory_Neuron-MGE2|Worl... 2.57732 [SYT1, FGF14, MEG3, NRXN3, CNTNAP2]
ToppCell Inhibitory 0.0 194 14 5 Comparison of human cortex and organoids Neuron-Postmitotic-Inhibitory_Neuron-MGE2-10|W... 2.57732 [SYT1, FGF14, MEG3, NRXN3, CNTNAP2]
ToppCell Neuron 0.0 199 14 5 Comparison of human cortex and organoids Neuron|World / Primary Cells by Cluster 2.512563 [CELF2, SYT1, MEG3, RBFOX1, PTPRD]
ToppCell Neuron-Postmitotic 0.0 199 14 5 Comparison of human cortex and organoids Neuron-Postmitotic|World / Primary Cells by Cl... 2.512563 [CELF2, SYT1, MEG3, RBFOX1, PTPRD]
ToppCell Excitatory 0.0 200 14 5 Comparison of human cortex and organoids Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... 2.5 [CELF2, SYT1, MEG3, RBFOX1, PTPRD]
ToppCell Fetal_brain-fetalBrain_Zhong_nature-GW16|GW16 0.0 200 14 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW16-Neuro... 2.5 [SYT1, MEG3, RBFOX1, PTPRD, CNTNAP2]
ToppCell Excitatory 0.0 200 14 5 Comparison of human cortex and organoids Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... 2.5 [CELF2, SYT1, MEG3, RBFOX1, PTPRD]
ToppCell Cortical_neuron|GW09 0.0 200 14 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-organoid_Tanaka_cellReport-GW09-Ne... 2.5 [CELF2, SYT1, RBFOX1, PTPRD, CNTNAP2]
ToppCell Fetal_brain-fetalBrain_Zhong_nature-GW23|GW23 0.0 112 14 4 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW23-Neuro... 3.571429 [SYT1, MEG3, NRXN3, RBFOX1]
2 ToppCell Oligodendrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 2.105263 [SH3TC2, SELENOP, KIF6, MBOAT1]
ToppCell OPC 0.000008 119 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-POLYDENDROCYTE-P1-P1_1-T... 2.521008 [JOSD2, SH3TC2, YWHAQ]
ToppCell OPC 0.000008 119 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-POLYDENDROCYTE-P1-P1_1|H... 2.521008 [JOSD2, SH3TC2, YWHAQ]
ToppCell OPC 0.000008 119 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-POLYDENDROCYTE-P1-P1_1-T... 2.521008 [JOSD2, SH3TC2, YWHAQ]
ToppCell Oligodendrocyte 0.000009 124 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Macroglia-OLIGODENDROCYTE|Pos... 2.419355 [SH3TC2, KCNK13, MBOAT1]
ToppCell Oligodendrocyte 0.000011 132 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-OLIGODENDROCYTE-O2-Trf|H... 2.272727 [JOSD2, SH3TC2, KCNK13]
ToppCell Oligodendrocyte 0.000011 132 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-OLIGODENDROCYTE-O2|Hippo... 2.272727 [JOSD2, SH3TC2, KCNK13]
ToppCell Oligodendrocyte 0.000012 137 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-OLIGODENDROCYTE|Hippocam... 2.189781 [JOSD2, SH3TC2, KCNK13]
ToppCell Oligodendrocyte 0.000029 183 15 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 1.639344 [SH3TC2, SELENOP, KIF6]
ToppCell Oligodendrocyte 0.00003 184 15 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 1.630435 [SH3TC2, SELENOP, KIF6]
ToppCell Oligodendrocyte 0.00003 185 15 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 1.621622 [SH3TC2, SELENOP, KIF6]
ToppCell Oligodendrocyte 0.00003 185 15 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 1.621622 [SH3TC2, SELENOP, KIF6]
ToppCell Oligodendrocyte 0.00003 186 15 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 1.612903 [SH3TC2, SELENOP, MBOAT1]
ToppCell Oligodendrocyte 0.000031 187 15 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 1.604278 [SH3TC2, SELENOP, KIF6]
ToppCell Oligodendrocyte 0.000031 187 15 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 1.604278 [SH3TC2, SELENOP, KIF6]
ToppCell Oligodendrocyte 0.000032 190 15 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 1.578947 [SH3TC2, SELENOP, MBOAT1]
ToppCell Excitatory 0.000036 197 15 3 Comparison of human cortex and organoids Neuron-Postmitotic-Excitatory_Neuron_-PFC|Worl... 1.522843 [SHTN1, YWHAQ, APP]
ToppCell 3.5_mon|organoid_Paulsen_bioRxiv 0.000036 197 15 3 Integration of Four Brain Organoid Datasets an... Brain_organoid-organoid_Paulsen_bioRxiv-3.5_mo... 1.522843 [SHTN1, JOSD2, TSC22D3]
ToppCell Excitatory 0.000036 197 15 3 Comparison of human cortex and organoids Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... 1.522843 [SHTN1, YWHAQ, APP]
ToppCell Excitatory 0.000036 197 15 3 Comparison of human cortex and organoids Neuron-Postmitotic-Excitatory_Neuron_-Upper_La... 1.522843 [SHTN1, YWHAQ, APP]
ToppCell Excitatory 0.000036 197 15 3 Comparison of human cortex and organoids Neuron-Postmitotic-Excitatory_Neuron_-PFC-29|W... 1.522843 [SHTN1, YWHAQ, APP]
ToppCell Oligodendrocyte 0.000038 200 15 3 Human Adult Brain MTG Macroglial-Oligodendrocytes|Macroglial / cells... 1.5 [SHTN1, SH3TC2, KIF6]
ToppCell Oligodendrocyte 0.000038 200 15 3 Human Adult Brain MTG Macroglial-Oligodendrocytes-OPALIN---|Macrogli... 1.5 [SHTN1, SH3TC2, KIF6]
ToppCell Oligodendrocyte 0.000038 200 15 3 Human Adult Brain MTG Macroglial-Oligodendrocytes-OPALIN-|Macroglial... 1.5 [SHTN1, SH3TC2, KIF6]
ToppCell Oligodendrocyte 0.000038 200 15 3 Human Adult Brain MTG Macroglial-Oligodendrocytes-OPALIN----L1-6|Mac... 1.5 [SHTN1, SH3TC2, KIF6]
ToppCell Oligodendrocyte 0.000038 200 15 3 Human Adult Brain MTG Macroglial-Oligodendrocytes-OPALIN--|Macroglia... 1.5 [SHTN1, SH3TC2, KIF6]
ToppCell Oligodendrocyte 0.000038 200 15 3 Human Adult Brain MTG Macroglial-Oligodendrocytes-OPALIN|Macroglial ... 1.5 [SHTN1, SH3TC2, KIF6]
ToppCell Oligodendrocyte 0.000465 96 15 2 Mouse Adult Brain Overview (690k cells, 9 regi... Cerebellum-Macroglia-OLIGODENDROCYTE-O2-Trf-Ol... 2.083333 [KIF6, KCNK13]
ToppCell Oligodendrocyte 0.000465 96 15 2 Mouse Adult Brain Overview (690k cells, 9 regi... Cerebellum-Macroglia-OLIGODENDROCYTE-O2-Trf|Ce... 2.083333 [KIF6, KCNK13]
ToppCell Oligodendrocyte 0.000465 96 15 2 Mouse Adult Brain Overview (690k cells, 9 regi... Cerebellum-Macroglia-OLIGODENDROCYTE-O2|Cerebe... 2.083333 [KIF6, KCNK13]
ToppCell Oligodendrocyte 0.000465 96 15 2 Mouse Adult Brain Overview (690k cells, 9 regi... Cerebellum-Macroglia-OLIGODENDROCYTE-O2-Trf-Ol... 2.083333 [KIF6, KCNK13]
ToppCell OPC 0.00062 111 15 2 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Macroglia-POLYDENDROCYTE-P1|Hippoc... 1.801802 [JOSD2, SH3TC2]
ToppCell Fetal_brain-fetalBrain_Zhong_nature-GW23|GW23 0.000631 112 15 2 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW23-Neuro... 1.785714 [SHTN1, YWHAQ]
ToppCell Oligodendrocyte 0.000665 115 15 2 Mouse Adult Brain Overview (690k cells, 9 regi... Cerebellum-Macroglia-OLIGODENDROCYTE|Cerebellu... 1.73913 [KIF6, KCNK13]
3 ToppCell Excitatory 0.0 200 13 4 Human Adult Brain MTG Neuronal-Excitatory-eC(RORB)|Neuronal / cells ... 2.0 [PTPRK, RORA, ESR1, PHLDB2]
ToppCell Excitatory 0.000015 170 13 3 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Neuronal-Glutam... 1.764706 [PTPRK, PHLDB2, KCNH8]
ToppCell Excitatory 0.000016 173 13 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... 1.734104 [PTPRK, UTRN, PHLDB2]
ToppCell Excitatory 0.000016 174 13 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... 1.724138 [PTPRK, UTRN, PHLDB2]
ToppCell Excitatory 0.000016 176 13 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Neuronal-Glutama... 1.704545 [PTPRK, UTRN, PHLDB2]
ToppCell Excitatory 0.000017 178 13 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... 1.685393 [PTPRK, RORA, ESR1]
ToppCell Excitatory 0.000017 179 13 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Neuronal-Glutama... 1.675978 [UTRN, RORA, PHLDB2]
ToppCell Excitatory 0.000017 180 13 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Neuronal-Glutama... 1.666667 [UTRN, PHLDB2, KCNH8]
ToppCell Excitatory 0.000017 180 13 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... 1.666667 [UTRN, RORA, PHLDB2]
ToppCell Inhibitory 0.000018 183 13 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Neuronal-Inh_GABAe... 1.639344 [CPED1, SDK1, GREB1]
ToppCell Excitatory 0.000018 183 13 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Neuronal-Glutamatergi... 1.639344 [PTPRK, RORA, ESR1]
ToppCell Excitatory 0.000019 185 13 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Neuronal-Glutamaterg... 1.621622 [UTRN, PHLDB2, KCNH8]
ToppCell Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 0.00002 188 13 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 1.595745 [PARD3B, ZBTB20, KCNH8]
ToppCell Inhibitory 0.00002 188 13 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Neuronal-Inh_GABAerg... 1.595745 [CPED1, SDK1, GREB1]
ToppCell Somatosensory_Cortex_(S1)-Non-neuronal|Somatos... 0.00002 189 13 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal|Somatos... 1.587302 [PARD3B, ZBTB20, KCNH8]
ToppCell primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 0.00002 189 13 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 1.587302 [PARD3B, ZBTB20, KCNH8]
ToppCell primary_auditory_cortex_(A1C)-Non-neuronal|pri... 0.00002 189 13 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal|pri... 1.587302 [PARD3B, ZBTB20, KCNH8]
ToppCell Inhibitory 0.000021 190 13 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Neuronal-Inh_GABAerg... 1.578947 [CPED1, SDK1, GREB1]
ToppCell Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 0.000021 190 13 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 1.578947 [PARD3B, ZBTB20, KCNH8]
ToppCell Excitatory 0.000021 190 13 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... 1.578947 [PTPRK, RORA, KCNH8]
ToppCell Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 0.000021 190 13 3 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 1.578947 [PARD3B, ZBTB20, KCNH8]
ToppCell Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 0.000021 190 13 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 1.578947 [PARD3B, ZBTB20, KCNH8]
ToppCell Primary_Motor_Cortex_(M1)-Non-neuronal|Primary... 0.000021 191 13 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal|Primary... 1.570681 [PARD3B, ZBTB20, KCNH8]
ToppCell Anterior_Cingulate_gyrus_(CgG)-Non-neuronal|An... 0.000021 191 13 3 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal|An... 1.570681 [PARD3B, ZBTB20, KCNH8]
ToppCell Mid-temporal_gyrus_(MTG)-Non-neuronal|Mid-temp... 0.000021 191 13 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal|Mid-temp... 1.570681 [PARD3B, ZBTB20, KCNH8]
ToppCell Excitatory 0.000021 192 13 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Neuronal-Glutamate... 1.5625 [PTPRK, RORA, KCNH8]
4 ToppCell primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 0.0 189 15 5 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 2.645503 [FOLH1, SELENOP, HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 200 15 5 Human Adult Brain MTG Macroglial-Astrocytes-SLC14A1|Macroglial / cel... 2.5 [FMN2, HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 200 15 5 Human Adult Brain MTG Macroglial-Astrocytes|Macroglial / cells hiera... 2.5 [FMN2, HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Macroglial|World / 0.0 200 15 5 Human Adult Brain MTG Macroglial|World / cells hierarchy compared to... 2.5 [FOLH1, PRRX1, HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 200 15 5 Human Adult Brain MTG Macroglial-Astrocytes-SLC14A1---|Macroglial / ... 2.5 [FMN2, HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 200 15 5 Human Adult Brain MTG Macroglial-Astrocytes-SLC14A1----L1-6|Macrogli... 2.5 [FMN2, HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 200 15 5 Human Adult Brain MTG Macroglial-Astrocytes-SLC14A1--|Macroglial / c... 2.5 [FMN2, HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 200 15 5 Human Adult Brain MTG Macroglial-Astrocytes-SLC14A1-|Macroglial / ce... 2.5 [FMN2, HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 186 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 2.150538 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 187 15 4 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 2.139037 [PRRX1, HIF3A, PSD2, STON2]
ToppCell Astrocyte 0.0 187 15 4 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 2.139037 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 187 15 4 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 2.139037 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 187 15 4 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 2.139037 [PRRX1, HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 187 15 4 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 2.139037 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 0.0 188 15 4 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 2.12766 [FOLH1, HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 2.105263 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 2.105263 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 2.105263 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 2.105263 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 2.105263 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 2.105263 [FOLH1, HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 190 15 4 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 2.105263 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Primary_Motor_Cortex_(M1)-Non-neuronal|Primary... 0.0 191 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal|Primary... 2.094241 [FOLH1, SELENOP, HIF3A, STON2]
ToppCell Anterior_Cingulate_gyrus_(CgG)-Non-neuronal|An... 0.0 191 15 4 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal|An... 2.094241 [FOLH1, HIF3A, ALDH1L1, STON2]
ToppCell Mid-temporal_gyrus_(MTG)-Non-neuronal|Mid-temp... 0.0 191 15 4 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal|Mid-temp... 2.094241 [FOLH1, HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 192 15 4 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 2.083333 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 192 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 2.083333 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 192 15 4 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 2.083333 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 193 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 2.072539 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.0 193 15 4 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 2.072539 [HIF3A, PSD2, ALDH1L1, STON2]
ToppCell Astrocyte 0.000024 172 15 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 1.744186 [HIF3A, PSD2, STON2]
ToppCell Astrocyte 0.000025 174 15 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 1.724138 [PRRX1, HIF3A, STON2]
ToppCell Posterior_cortex-Macroglia|Posterior_cortex 0.000025 175 15 3 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Macroglia|Posterior_cortex / ... 1.714286 [FOLH1, ADHFE1, STON2]
ToppCell Astrocyte 0.000028 181 15 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 1.657459 [HIF3A, PSD2, STON2]
ToppCell Astrocyte 0.00003 184 15 3 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 1.630435 [HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.00003 186 15 3 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 1.612903 [HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.000031 187 15 3 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 1.604278 [HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.000031 187 15 3 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 1.604278 [HIF3A, ALDH1L1, STON2]
ToppCell Astrocyte 0.000031 187 15 3 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 1.604278 [HIF3A, ALDH1L1, STON2]
ToppCell Somatosensory_Cortex_(S1)-Non-neuronal|Somatos... 0.000032 189 15 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal|Somatos... 1.587302 [FOLH1, HIF3A, STON2]
ToppCell primary_auditory_cortex_(A1C)-Non-neuronal|pri... 0.000032 189 15 3 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal|pri... 1.587302 [FOLH1, HIF3A, STON2]
ToppCell Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 0.000032 190 15 3 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 1.578947 [FOLH1, SELENOP, HIF3A]
6 ToppCell Neuronal|GW08 0.000152 59 14 2 Integration of Four Brain Organoid Datasets an... Fetal_brain-organoid_Tanaka_cellReport-GW08-Ne... 3.389831 [PTEN, NCDN]
ToppCell Fetal_brain-fetalBrain_Zhong_nature-GW08|GW08 0.00028 80 14 2 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW08-Neuro... 2.5 [PTEN, NCDN]
ToppCell Neuronal-ventral_progenitors_and_neurons_1|Org... 0.001182 165 14 2 Integration of Four Brain Organoid Datasets an... Brain_organoid-organoid_Kanton_Nature-Organoid... 1.212121 [NCDN, AGAP2]
ToppCell Excitatory 0.001225 168 14 2 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Neuronal-Excitatory-eN1(Slc17a7)|H... 1.190476 [FKBP1A, NCDN]
ToppCell Excitatory 0.001545 189 14 2 Mouse Adult Brain Overview (690k cells, 9 regi... Hippocampus-Neuronal-Excitatory|Hippocampus / ... 1.058201 [NCDN, AGAP2]
7 ToppCell OPC 0.0 189 13 8 Comparison of human cortex and organoids Non-neuronal-Non-dividing-OPC|World / Primary ... 4.232804 [SOX6, PDGFRA, PTPRZ1, SGCD, GRID2, LHFPL3, EP...
ToppCell OPC 0.0 189 13 8 Comparison of human cortex and organoids Non-neuronal-Non-dividing-OPC-OPC|World / Prim... 4.232804 [SOX6, PDGFRA, PTPRZ1, SGCD, GRID2, LHFPL3, EP...
ToppCell OPC 0.0 189 13 8 Comparison of human cortex and organoids Non-neuronal-Non-dividing-OPC-OPC-30|World / P... 4.232804 [SOX6, PDGFRA, PTPRZ1, SGCD, GRID2, LHFPL3, EP...
ToppCell OPC 0.0 187 13 7 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 3.743316 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 187 13 7 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Macro... 3.743316 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 200 13 7 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA----L1-6|Macr... 3.5 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 200 13 7 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA|Macroglial /... 3.5 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 200 13 7 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA---|Macroglia... 3.5 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 200 13 7 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA-|Macroglial ... 3.5 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 200 13 7 Human Adult Brain MTG Macroglial-Polydendrocytes-PDGFRA--|Macroglial... 3.5 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 200 13 7 Human Adult Brain MTG Macroglial-Polydendrocytes|Macroglial / cells ... 3.5 [SOX6, PDGFRA, PTPRZ1, GRID2, MEGF11, LHFPL3, ...
ToppCell OPC 0.0 182 13 6 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 3.296703 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 182 13 6 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 3.296703 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 182 13 6 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-Ma... 3.296703 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 182 13 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 3.296703 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 182 13 6 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Macrogl... 3.296703 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 182 13 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Macrogl... 3.296703 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 185 13 6 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 3.243243 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 185 13 6 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-Mac... 3.243243 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 185 13 6 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 3.243243 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 185 13 6 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Macrogli... 3.243243 [SOX6, PDGFRA, PTPRZ1, MEGF11, LHFPL3, EPN2]
ToppCell OPC 0.0 192 13 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW16-OPC_r... 2.604167 [SOX6, PDGFRA, SGCD, GRID2, LHFPL3]
ToppCell OPC 0.0 192 13 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW16-OPC_r... 2.604167 [SOX6, PDGFRA, SGCD, GRID2, LHFPL3]
ToppCell OPC 0.0 200 13 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW26-OPC_r... 2.5 [PDGFRA, PTPRZ1, GRID2, LHFPL3, EPN2]
ToppCell OPC 0.0 200 13 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW26-OPC_r... 2.5 [PDGFRA, PTPRZ1, GRID2, LHFPL3, EPN2]
ToppCell Astrocyte 0.0 200 13 5 Integration of Four Brain Organoid Datasets an... Fetal_brain-organoid_Tanaka_cellReport-GW16-Ma... 2.5 [PDGFRA, PTPRZ1, GRID2, LHFPL3, SLC35F1]
8 ToppCell Microglial 0.0 200 12 8 Human Adult Brain MTG Hematolymphoid-Microglia|Hematolymphoid / cell... 4.0 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, LY86, ME...
ToppCell Microglial 0.0 200 12 8 Human Adult Brain MTG Hematolymphoid-Microglia-TYROBP----L1-3|Hemato... 4.0 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, LY86, ME...
ToppCell Microglial 0.0 200 12 8 Human Adult Brain MTG Hematolymphoid-Microglia-TYROBP--|Hematolympho... 4.0 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, LY86, ME...
ToppCell Microglial 0.0 200 12 8 Human Adult Brain MTG Hematolymphoid-Microglia-TYROBP---|Hematolymph... 4.0 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, LY86, ME...
ToppCell Microglial 0.0 200 12 8 Human Adult Brain MTG Hematolymphoid-Microglia-TYROBP-|Hematolymphoi... 4.0 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, LY86, ME...
ToppCell Microglial 0.0 200 12 8 Human Adult Brain MTG Hematolymphoid-Microglia-TYROBP|Hematolymphoid... 4.0 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, LY86, ME...
ToppCell Microglial | Endothelial 0.0 187 12 7 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-End... 3.743316 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, GPR34, L...
ToppCell Microglial | Endothelial 0.0 187 12 7 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-End... 3.743316 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, GPR34, L...
ToppCell Microglial | Endothelial 0.0 187 12 7 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-End... 3.743316 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, GPR34, L...
ToppCell Microglial | Endothelial 0.0 188 12 7 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-En... 3.723404 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86, MERTK]
ToppCell Microglial | Endothelial 0.0 188 12 7 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-En... 3.723404 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86, MERTK]
ToppCell Endothelial 0.0 188 12 7 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-En... 3.723404 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86, MERTK]
ToppCell Microglial | Endothelial 0.0 188 12 7 Human Adult Multiple Cortical Areas SMART-seq Anterior_Cingulate_gyrus_(CgG)-Non-neuronal-En... 3.723404 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86, MERTK]
ToppCell Endothelial 0.0 190 12 7 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Endothe... 3.684211 [DOCK2, INPP5D, LRMDA, SRGAP2, DOCK8, GPR34, L...
ToppCell Microglial 0.0 199 12 7 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW23-Myelo... 3.517588 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86, MERTK]
ToppCell Myeloid|GW23 0.0 199 12 7 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW23-Myelo... 3.517588 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86, MERTK]
ToppCell Microglial 0.0 179 12 6 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Hematopoietic-MICROGLIA|Poste... 3.351955 [DOCK2, HEXB, INPP5D, TGFBR1, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 184 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Endot... 3.26087 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 184 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Endot... 3.26087 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 184 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Endot... 3.26087 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Endothelial 0.0 185 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Visual_cortex_(V1C)-Non-neuronal-Endot... 3.243243 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 186 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Endothe... 3.225806 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 186 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Endothe... 3.225806 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 186 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Endothe... 3.225806 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Endothelial 0.0 187 12 6 Human Adult Multiple Cortical Areas SMART-seq Primary_Motor_Cortex_(M1)-Non-neuronal-Endothe... 3.208556 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Endothelial 0.0 187 12 6 Human Adult Multiple Cortical Areas SMART-seq primary_auditory_cortex_(A1C)-Non-neuronal-End... 3.208556 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 188 12 6 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Endothel... 3.191489 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 188 12 6 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Endothel... 3.191489 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 188 12 6 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Endothel... 3.191489 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 189 12 6 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Endothe... 3.174603 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Endothelial 0.0 189 12 6 Human Adult Multiple Cortical Areas SMART-seq Mid-temporal_gyrus_(MTG)-Non-neuronal-Endothel... 3.174603 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 189 12 6 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Endothe... 3.174603 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial | Endothelial 0.0 189 12 6 Human Adult Multiple Cortical Areas SMART-seq Somatosensory_Cortex_(S1)-Non-neuronal-Endothe... 3.174603 [DOCK2, INPP5D, LRMDA, DOCK8, GPR34, LY86]
ToppCell Microglial 0.0 198 12 6 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW26-Myelo... 3.030303 [DOCK2, INPP5D, DOCK8, GPR34, LY86, MERTK]
ToppCell Myeloid|GW26 0.0 198 12 6 Integration of Four Brain Organoid Datasets an... Fetal_brain-fetalBrain_Zhong_nature-GW26-Myelo... 3.030303 [DOCK2, INPP5D, DOCK8, GPR34, LY86, MERTK]
ToppCell Microglial 0.0 127 12 5 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Hematopoietic-MICROGLIA-MI2(C... 3.937008 [HEXB, INPP5D, DOCK8, GPR34, LY86]
ToppCell Microglial 0.0 166 12 5 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Hematopoietic-MICROGLIA-MI2(C... 3.012048 [HEXB, INPP5D, DOCK8, GPR34, LY86]
ToppCell Microglial 0.0 166 12 5 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Hematopoietic-MICROGLIA-MI2(C... 3.012048 [HEXB, INPP5D, DOCK8, GPR34, LY86]
ToppCell Microglial 0.0 166 12 5 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Hematopoietic-MICROGLIA-MI2(C... 3.012048 [HEXB, INPP5D, DOCK8, GPR34, LY86]
ToppCell Posterior_cortex-Hematopoietic|Posterior_cortex 0.0 178 12 5 Mouse Adult Brain Overview (690k cells, 9 regi... Posterior_cortex-Hematopoietic|Posterior_corte... 2.808989 [DOCK2, HEXB, INPP5D, GPR34, LY86]

Annotate with CellTypist¶

In [ ]:
# %%time

# # To Aggregate More Specific Cell Types
# celltypist_rename = dict(
#     Gabaergic=["GABA"], Glutamatergic=["Glut"],
#     Dopaminergic=["Dopa"], Serotonergic=["Sero"],
#     Inhibitory=["Inh"], Excitatory=["Exc"],
#     Astrocyte=["Astro"],
#     Microglia=["Microglia"],
#     Pericyte=["peri"],
#     Monocyte=["Monocyte"],
#     Lymphoid=["Lymphoid"],
#     Endothelial=["Endothelial"],
#     Oligodendrocyte=[r"^(?=.*oligo)(?!.*poly)(?!.*opc).*"],
#     Polydendrocyte=["OPC"])

# # Run CellTypist
# self.rna.X = self.rna.layers["counts"].copy()
# sc.pp.normalize_total(self.rna, target_sum=10000)
# sc.pp.log1p(self.rna) # copy=True: do not update adata.X
# predictions = self.annotate(
#     model_celltypist, col_celltype=col_celltype, layer=None,
#     col_celltype_new="", majority_voting=True, min_prop=0.5, use_GPU=True)
# if "majority_voting" in self.rna.obs:
#     self.rna.obs.loc[:, "majority_voting_short"] = self.rna.obs[
#         "majority_voting"].apply(lambda x: " ".join(x.split(
#             " ")[1:]) if all((i in [str(i) for i in np.arange(
#                 0, 10)] for i in x.split(" ")[
#                     0])) else x)  # drop pointless #s in front of cell types

# # Rename Cell Types
# rn_ct = predictions.predicted_labels.groupby("majority_voting").apply(
#     lambda x: {x.name: " | ".join([
#         j for j in celltypist_rename if any((re.search(
#             i.lower(), x.name.lower()) for i in celltypist_rename[
#                 j]))])}).apply(lambda x: {list(x.keys())[0]: list(x.keys(
#                     ))[0]} if x[list(x.keys())[0] ] == "" else x).apply(
#                         lambda x: pd.Series(x)).stack().reset_index(
#                             0, drop=True)
# if "annotation_majority_voting" in self.rna.obs:
#     self.rna.obs = self.rna.obs.drop("annotation_majority_voting", axis=1)
# self.rna.obs = self.rna.obs.join(self.rna.obs.replace({
#     "majority_voting": dict(rn_ct)})["majority_voting"].to_frame(
#         "annotation_majority_voting"))
# self.rna.X = self.layers["scaled"].copy()

Annotate with Map My Cells¶

  • Make sure to run the following bash commonds after activating the conda environment used for this notebook.

  • Pull cell_type_mapper from GitHub (clone into your home directory): cd && git clone git@github.com:AllenInstitute/cell_type_mapper.git

  • Navigate to that directory and run pip install .

  • Navigate to the folder containing this notebook.

  • Install ABC Atlas (while in same directory as this notebook): pip install -U git+https://github.com/alleninstitute/abc_atlas_access >& scratch/junk.txt

  • Pull lookup files (while in same directory as this notebook):

cd resources
wget https://allen-brain-cell-atlas.s3-us-west-2.amazonaws.com/mapmycells/WMB-10X/20240831/mouse_markers_230821.json
wget https://allen-brain-cell-atlas.s3-us-west-2.amazonaws.com/mapmycells/WMB-10X/20240831/precomputed_stats_ABC_revision_230821.h5

Note: To use GPU + Torch, you may need to alter the file "cell_type_mapper/src/cell_type_mapper/cell_by_gene/cell_by_gene.py" line np.where(np.logical_not(np.isfinite(data)))[0] to read instead

try:
    nan_rows = np.where(
        np.logical_not(np.isfinite(data.cpu().numpy())))[0]
except Exception:
    nan_rows = np.where(np.logical_not(np.isfinite(data)))[0]

You may have to run the following code in this notebook:

os.environ["NUMEXPR_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OMP_NUM_THREADS"] = "1"

and

`_correlation_dot_gpu()` in distance_utils.py change
`correlation = torch.matmul(arr0, arr1)` to
try:
    correlation = torch.matmul(arr0, arr1)
except RuntimeError as err:
    if "CUBLAS_STATUS_NOT_INITIALIZED" in str(err):
        arr0_cpu = arr0.cpu()
        arr1_cpu = arr1.cpu()
        correlation = torch.matmul(arr0_cpu, arr1_cpu).to(arr0.device)
    else:
        raise

to manage processes/memory.

In [ ]:
# %%time

# # Write File to Use as Input for Map My Cells
# if overwrite is True or not os.path.exists(file_new):
#     os.makedirs("data", exist_ok=True)
#     self.rna.X = self.rna.layers["counts"]
#     self.rna.write_h5ad(file_new)
# else:
#     raise ValueError("Must be able to overwrite to run Map My Cells.")

# # Run Map My Cells
# self.rna = scflow.pp.run_mapbraincells(
#     file_new, map_my_cells_source=map_my_cells_source,
#     dir_scratch="scratch", dir_resources="resources",
#     validate_output_file="scratch/tmp.h5ad",  # map_to_ensembl=True,
#     map_my_cells_region_keys=map_my_cells_region_keys,
#     map_my_cells_cell_keys=map_my_cells_cell_keys, verbose_stdout=True,
#     n_processors=4, chunk_size=5000, max_gb=5)

# View Results
# _ = self.plot(kind="umap", color=["cellmap_class_name"])
# if "annotation_toppgene" in self.rna.obs:
#     print(self.rna.obs[["cellmap_class_name", "annotation_toppgene"]
#                        ].value_counts().sort_index())
# self.rna.obs[[i for i in self.rna.obs if "cellmap" in i and "ori" not in i]]

Compare Annotations¶

In [38]:
cols = [i for i in ["annotation_by_overlap", "annotation_toppgene",
                    "annotation_majority_voting"] if i in self.rna.obs]
# cols += [i for i in [
#     "cellmap_class_name", "cellmap_subclass_name"] if i in self.rna.obs]

# Plot UMAPs
self.plot(kind="umap", color=cols, wspace=0.3)

# Compare
self.rna.obs[cols].value_counts().sort_index()
Out[38]:
annotation_by_overlap  annotation_toppgene
Neuron                 OPC                    21527
                       1                      19639
                       Excitatory              8753
                       5                      19269
Oligodendrocyte        Oligodendrocyte        18451
Endothelial            Excitatory              2158
Astrocyte              Astrocyte               7160
OPC                    OPC                     3117
Microglial             Microglial              3200
Name: count, dtype: int64
No description has been provided for this image

OSD-613-Specific¶

Clean variable names & keys

In [39]:
self.rna.obs.loc[:, "Age_Start"] = self.rna.obs["Factor Value[Age]"].astype(
    str) + " Weeks"
self.rna.obs.loc[:, "Age_End"] = self.rna.obs[
    "Characteristics[Age at Euthanasia]"].astype(str) + " Weeks"
self.rna.obs.loc[:, "Condition"] = self.rna.obs["Factor Value[Spaceflight]"]
if "cellmap_class_name" in self.rna.obs:  # cell name w/o region cues
    self.rna.obs.loc[:, "cellmap_class_name_collapsed"] = self.rna.obs[
        "cellmap_class_name"].apply(lambda x: x + "***" + str(
            "Glutamatergic-" if ("glut" in x.lower()) else "") + str(
                "GABAergic-" if "gaba" in x.lower() else "") + str(
                    "Oligodendrocyte-" if "oligo" in x.lower() else "") + str(
                        "Cholinergic-" if "chol" in x.lower() else ""))
    self.rna.obs.loc[:, "cellmap_class_name_collapsed"] = self.rna.obs[
        "cellmap_class_name_collapsed"].apply(
            lambda x: x.split("***")[0 if x.split("***")[1] == "" else 1])
    self.rna.obs.loc[:, "cellmap_class_name_collapsed"] = self.rna.obs[
        "cellmap_class_name_collapsed"].apply(
            lambda x: x[:-1] if x[-1] == "-" else x)

Test¶

Make sure preprocessing properly implemented

In [ ]:
# for x in kws_pp:
#     print(f"\n\n{'=' * 80}\n{x}\n{'=' * 80}\n")
#     ann = self.rna[self.rna.obs[col_sample] == x]
#     if join_method == "inner":
#         assert all(ann.var["n_cells_by_counts"] >= kws_pp[
#             x]["min_max_cells"][0])
#         assert all(ann.var["n_cells_by_counts"] <= kws_pp[
#             x]["min_max_cells"][1]) if kws_pp[x]["min_max_cells"][1] else True
#     assert all(ann.obs["n_genes"] >= kws_pp[x]["min_max_genes"][0])
#     assert all(ann.obs["n_genes"] <= kws_pp[x][
#         "min_max_genes"][1]) if kws_pp[x]["min_max_genes"][1] else True
#     assert all(ann.obs["pct_counts_mt"] <= kws_pp[x]["max_mt"])
#     assert all(ann.obs["total_counts"] >= kws_pp[x]["min_max_counts"][0])
#     assert all(ann.obs["total_counts"] <= kws_pp[x]["min_max_counts"][1]) if (
#         kws_pp[x]["min_max_counts"][1]) else True
#     print(kws_pp[x])
#     print(ann.obs[["n_genes", "pct_counts_mt", "total_counts"]
#                   ].describe().loc[["min", "max"]])
#     print(ann.var[["n_cells_by_counts"]].describe().loc[[
#         "min", "max"]])

Final Write¶

In [ ]:
# Descriptives
if all((i in self.rna.obs for i in [
        "Factor Value[Age]", "Characteristics[Age at Euthanasia]"])):
    print(self.rna.obs[["Factor Value[Age]",
                        "Characteristics[Age at Euthanasia]"]].value_counts())

# Write h5ad
self.rna.X = self.rna.layers["counts"].copy()
if overwrite is True or not os.path.exists(file_new):
    print("\n\n", f"Writing file to {file_new}...")
    self.rna.write_h5ad(file_new)

# Write Version Compatible with Older Packages
adata = self.rna.copy()
adata.uns = {}
adata.write_h5ad(os.path.splitext(file_new)[0] + "_compatible.h5ad")

# Send Email with Output When Done
if email is not None and html_out is not None:
    os.system(f"jupyter nbconvert --to html {cur_file}")
    os.system(f"echo 'yay' | mutt -s 'JOB DONE' -a {html_out} -- {email}")

Factor Value[Age]  Characteristics[Age at Euthanasia]
29                 37                                    55266
12                 20                                    48008
Name: count, dtype: int64


 Writing file to data/OSD-613_integrated.h5ad...

Scratch (IGNORE)¶

In [ ]:
# # Integrate
# join_method = "inner"
# cct = "leiden_individual" if kws_cluster is not None else None
# kws_integrate = {"col_celltype": cct,
#                  "flavor": "scanvi",
#                  # "flavor": "scvi",
#                  # "flavor": "scanorama",
#                  # "flavor": "harmony",
#                 "kws_pp": None, "kws_cluster": None, "n_top_genes": 1000,
#                  "join": join_method, "merge": "same",
#                  "col_batch": None,  # suppress using batch as covariate
#                  "drop_non_hvgs": False,
#                  "use_rapids": True,
#                  "fill_value": np.nan if join_method == "outer" else None,
#                  "out_file": file_new}
# self = scflow.Rna(sc.read(file_new), col_sample=col_sample,
#                   col_batch=col_batch, kws_integrate=kws_integrate)

# # Write Files for Processed/Integrated Objects?
# if overwrite is True or not os.path.exists(file_new):
#     self.rna.write_h5ad(file_new)

# # Display
# print(self.rna)
# self.rna.obs